# Chapter 11: Key Moments in LLM Knowledge Engineering

> **In one sentence:** A chronological record of the key moments that shaped LLM knowledge engineering from 2022 to 2026.
>
> **Why it matters:** Context for everything in this guide -- when it happened, who did it, and what changed.

![LLM Knowledge Engineering Timeline 2022-2026](../diagrams/timeline.png)

A chronological map of the events, releases, and ideas that shaped how we build knowledge systems with large language models. Where precise dates are known, they are included. Where only a quarter or month is confirmed, that granularity is used.

This is a **curated** timeline, not an exhaustive one. An event makes it onto this list only when it introduces, validates, or operationalizes a primitive, pattern, or narrative beat the framework tracks. Many notable releases — model version bumps, funding rounds, vertical products — are deliberately absent. See [CONTRIBUTING.md](../CONTRIBUTING.md) for the full inclusion criteria.

---

## 2022

### November 30 --- ChatGPT Launches

OpenAI releases ChatGPT, a conversational interface to GPT-3.5. It reaches 100 million users within two months, the fastest consumer technology adoption in history. The "prompt engineering" era begins: users discover that how you ask matters as much as what you ask.

---

## 2023

### Q1-Q2 --- The RAG Frameworks Emerge

**LangChain** and **LlamaIndex** gain rapid adoption as the first developer frameworks for building LLM applications. Retrieval-Augmented Generation (RAG) becomes the standard pattern for grounding LLM responses in external knowledge. The architecture is simple but transformative: retrieve relevant documents, inject them into the prompt, generate a grounded response. For the first time, LLMs can answer questions about private data they were never trained on.

### November --- GPT-4 Turbo and the 128K Context Window

OpenAI announces GPT-4 Turbo with a 128K token context window at DevDay. This 4x expansion over GPT-4's 32K window changes the economics of knowledge retrieval: entire documents can now fit in context, reducing the need for chunking and retrieval in some use cases. The "just put it all in context" school of thought begins to form.

### December --- Gemini 1.0 Launch

Google launches Gemini 1.0, its first natively multimodal model. Built from the ground up to process text, images, audio, and video, it signals that knowledge engineering will need to handle more than text.

---

## 2024

### February --- Gemini 1.5 and the 1M Token Context Window

Google releases Gemini 1.5 Pro with a 1-million-token context window. This is not incremental --- it is a paradigm shift. An entire codebase, a full book, hours of video transcript can now fit in a single prompt. The "RAG vs. long context" debate intensifies. Practitioners begin to realize that long context does not eliminate retrieval; it changes what retrieval is for.

### March --- Kimi Achieves 2M Chinese Character Context

Moonshot AI's Kimi reaches a 2-million Chinese character context window, the longest for any model optimized for Chinese text. This enables full-document processing for Chinese enterprise use cases --- legal contracts, regulatory filings, technical manuals --- that were previously impractical.

### November --- Anthropic Releases the Model Context Protocol (MCP)

Anthropic publishes MCP as an open specification for connecting LLMs to external tools and data sources. MCP standardizes how models discover, authenticate with, and invoke tools --- replacing the fragmented landscape of custom function-calling implementations. It is designed as a protocol, not a product: any model provider or tool developer can implement it. The analogy to USB or HTTP is deliberate. This moment marks the beginning of interoperable knowledge infrastructure.

---

## 2025

### January --- DeepSeek R1 and the Open-Source Inflection

DeepSeek releases R1, an open-weight reasoning model that matches OpenAI's o1 on key benchmarks. Trained for under $6 million and released under the MIT license, it demonstrates that frontier reasoning capabilities are no longer exclusive to well-funded labs. Enterprise open-source model adoption in China surges from 23% to 67%. The knowledge engineering implication: the foundation model is no longer the bottleneck or the moat.

### Mid-2025 --- "Context Engineering" Enters the Lexicon

Andrej Karpathy publicly endorses the term **"context engineering"** as a replacement for "prompt engineering," arguing that the discipline has evolved far beyond crafting individual prompts. The shift reflects a broader recognition: what matters is not the prompt in isolation, but the entire context assembly pipeline --- what gets loaded, when, in what order, and why. The community adopts the framing quickly.

### July --- Manus Publishes Context Engineering Lessons

Manus, the autonomous agent startup, publishes a detailed blog post on their approach to context engineering. Key insights include: keeping the context window as a "todo list" that naturally shrinks as tasks complete, using file system operations as extended memory, and the importance of "context engineering is the art of delivering the right information in the right format at the right time." The post becomes a reference document for practitioners.

### September --- Notion 3.0: The AI Agent Rebuild

Notion releases version 3.0, rebuilt from the ground up as an AI-native workspace. The core knowledge management features --- databases, pages, relations --- are re-architected to work seamlessly with AI agents. This is significant not as a single product launch but as evidence that mainstream productivity tools are converging on the knowledge harness pattern: structured knowledge + agent orchestration + context engineering.

### October --- Anthropic Launches Agent Skills

Anthropic introduces a skill system for Claude, allowing users to define reusable, composable capabilities that agents can discover and invoke. Skills are structured as markdown files with YAML frontmatter defining triggers, parameters, and dependencies. This formalizes the pattern that power users had been building informally: libraries of reusable agent behaviors.

### November --- MCP Crosses 8M Downloads

The Model Context Protocol reaches 8 million SDK downloads, up from near zero twelve months earlier. The protocol has become the de facto standard for LLM-to-tool integration, with implementations across every major model provider and hundreds of tool servers.

### December --- A Defining Month

Several developments converge in December 2025:

- **Anthropic publishes an open standard for skills** at agentskills.io, proposing a shared format for agent capabilities that any platform can implement.
- **MCP is donated to the Linux Foundation**, cementing its status as a vendor-neutral protocol maintained by the open-source community.
- **Progressive disclosure** is adopted by major platforms as the standard approach to skill and context management, validating the pattern independently developed by multiple teams.
- **Meta acquires Manus** for approximately $2 billion, signaling that autonomous agent infrastructure is now a strategic asset at the largest scale.

---

## 2026

### January --- ERNIE 5.0 and Kimi K2.5

- **ERNIE 5.0** launches from Baidu: 2.4 trillion parameters with native full-modality (text, image, audio, video in a single model).
- **Kimi K2.5** introduces Agent Swarm: a single query can spawn up to 100 sub-agents coordinating through approximately 1,500 tool calls. This is not a demo --- it is a production feature for complex multi-step research tasks.

### February --- Heinrich's Skill Graphs Post

A post by Heinrich (`@arscontexta`) on skill graphs --- how to organize, compose, and route between agent skills using graph structures --- went viral in early 2026, crossing into the broader AI-engineering audience. The reception indicates that the practitioner community is hungry for architectural patterns, not just model capabilities. Skill routing and composition emerge as first-class engineering concerns. Original post: [https://x.com/arscontexta/status/2023957499183829467](https://x.com/arscontexta/status/2023957499183829467).

### March --- The Research Papers

Two significant papers are published:

- **"Meta-Harness"** demonstrates a 6x performance gap between naive and well-engineered harnesses using identical foundation models. The paper provides empirical evidence that harness engineering matters more than model selection for production knowledge systems.
- **"SkillReducer"** addresses the emerging problem of skill proliferation, proposing techniques for managing systems with 55,000+ skills through automatic deduplication, clustering, and hierarchical organization.

### Q1 --- MCP at Scale

MCP reaches 97 million monthly SDK downloads, a 12x increase from November 2025. The protocol is now embedded in CI/CD pipelines, IDE extensions, enterprise platforms, and consumer applications. The interoperability promise is being realized: a tool server written for one model works with any model.

### April 2026 --- Initial Publication

This research report is published, attempting to synthesize the preceding four years of rapid development into a coherent framework for practitioners. The field continues to evolve faster than any single document can capture.

### March 31, 2026 --- Claude Code Source Leak

Anthropic accidentally publishes a 59.8MB source map inside the npm package `@anthropic-ai/claude-code` v2.1.88, exposing approximately 513,000 lines of unobfuscated TypeScript across 1,906 files. The leak reveals a three-layer **"Self-Healing Memory"** architecture, with `MEMORY.md` acting as a lightweight pointer-style index rather than a full store. It also exposes the **KAIROS** feature flag --- an autonomous daemon mode referenced more than 150 times in the source --- alongside 44 other hidden feature flags for unreleased capabilities. Anthropic characterizes the incident as a "human packaging error, not a security breach." Even so, the leak delivers the first empirical look at what a production harness actually contains, and the term "harness engineering" begins appearing in vendor marketing and job descriptions in the days that follow. (Included here for April context.)

### April 2026 --- The Open-Source Harness Builder Wave

The harness-as-moat thesis cut both ways in April 2026: as the practitioner term hardened into job descriptions, two open-source projects --- one in major rewrite, one brand-new --- accelerated past the 10K-stars threshold and gave the discipline its first widely adopted reference implementations. **Archon** (`coleam00/Archon`, MIT-licensed, originally launched February 2025) shipped **v2.1** in April as a TypeScript / Bun rewrite, wrapping Claude Code and the OpenAI Codex CLI in YAML-defined directed acyclic workflows that aim to make AI coding "deterministic and repeatable." It crossed 19K GitHub stars during the month. **OpenHarness** (`HKUDS/OpenHarness`, MIT-licensed, first commit April 1, 2026) shipped from the Hong Kong University of Science Data lab as an open Python implementation focused on agent harness internals --- skill systems, MCP HTTP transport, swarm polling --- with a built-in personal-agent demo ("Ohmo!"); it crossed 11K stars in its first three weeks. Read together, the wave matters less for any single feature than as a signal that the harness layer now has the kind of community-driven OSS gravity that 2023's RAG frameworks (LangChain, LlamaIndex) provided for the knowledge layer. The next twelve months of harness engineering will likely happen in public on these and similar projects.

### April 2, 2026 --- Microsoft MAI Models Launch

Microsoft releases three first-party multimodal foundation models via Foundry: **MAI-Transcribe-1**, **MAI-Voice-1**, and **MAI-Image-2**. All three ship with built-in guardrails and enterprise-grade governance. This is Microsoft's first serious entry into the multimodal foundation model market as a model provider (not just a distributor of OpenAI's work), and it puts them in direct competition with OpenAI, Google, and Anthropic on voice, transcription, and image generation. These are multimodal models --- not safety harnesses.

### April 3, 2026 --- The AI Velocity Paradox Report

Harness.io and Infosys jointly publish the **"State of DevOps 2026"** report. The headline finding: **69% of teams report deployment bottlenecks despite 45% faster AI-assisted coding**. The report frames this as the "AI Velocity Paradox" --- generation speed is no longer the limiting factor, but evaluation, review, and governance have not kept pace. The narrative begins to shift from "generation speed" to "evaluation and governance" as the new bottleneck.

### April 5, 2026 --- MemPalace and Spatial-Metaphor Memory

The agent-memory architecture race got a viral entry. **MemPalace** (`MemPalace/mempalace`, MIT-licensed, first commit April 5, 2026) launches with a deliberately old-fashioned framing: the memory store is organized as a **spatial hierarchy --- Wings → Rooms → Closets → Drawers ---** modeled on the classical *method of loci* used by mnemonists for two millennia. Each leaf "drawer" stores a verbatim text fragment, and navigation through the hierarchy itself serves as the index. The project's tagline ("the best-benchmarked open-source AI memory system") is backed by a reported **96.6% Recall@5 on LongMemEval**, the longest-published context-recall benchmark for agent memory; community reception is equally striking, with the repository accumulating roughly **49,000 GitHub stars in three weeks** --- the fastest-growing memory project of 2026 by an order of magnitude. The architecture is conceptually orthogonal to Mem0ᵍ (graph triplets), Titans (trainable neural module), and ByteRover (hierarchical markdown trees): MemPalace argues that *spatial* metaphor with *verbatim-first* storage is itself a retrieval primitive, and that the cognitive ergonomics of "remembering where you put it" matter as much as embedding similarity. An academic critique (arXiv 2604.21284, *"Spatial Metaphors for LLM Memory: A Critical Analysis of the MemPalace Architecture"*) was already in circulation before the month was out --- itself a signal of how rapidly the project moved into the field's central conversation.

### April 7, 2026 --- Claude Mythos Preview

Anthropic releases **Claude Mythos Preview** (not Claude 4.6), scoring **93.9% on SWE-bench Verified** --- a 13.1 point leap over Opus 4.6's 80.8% --- and 77.8% on SWE-bench Pro. Mythos is not publicly available. It is reserved for the **Project Glasswing** coalition (Apple, Google, Microsoft, Amazon, and others) for cybersecurity research, with approximately 40 whitelisted teams getting access at $25 / $125 per million input / output tokens. This is the first time Anthropic has gated a flagship tier behind a research coalition rather than a public preview.

Within approximately 14 hours of the Glasswing program being publicly announced, Anthropic disclosed unauthorized access to Claude Mythos Preview "through one of our third-party vendor environments." The breach is significant for the harness narrative for two reasons. First, it makes concrete what "harness as security perimeter" means in practice: the model itself was not compromised, but the *distribution harness* --- the third-party vendor stack used to gate Glasswing access --- was. Second, it foreshadows the inversion of the cyber-defense thesis behind Mythos: a model whose stated job is to find vulnerabilities for defenders was itself reachable through the supply chain that fronted it.

The Glasswing distribution pattern --- a vetted coalition for dual-use AI --- got a second instance on **May 11, 2026** when OpenAI shipped **GPT-5.5 with "Trusted Access for Cyber,"** extending availability through a coalition of EU partners (Deutsche Telekom, BBVA, Telefonica, Sophos, Scalable Capital) with required Advanced Account Security enforcement. The pattern reproducing across vendors confirms that vetted-coalition distribution for dual-use AI is now an industry convention, not a one-off.

### April 8, 2026 --- Anthropic Managed Agents (Public Beta)

Anthropic ships **Claude Managed Agents** as a public-beta hosted runtime for long-horizon agent work. Where the Advisor Tool (April 2026) productized *what to think about* and Claude Opus 4.7's task budgets (April 16) productized *how hard to think*, Managed Agents productizes *where the loop runs*. The Anthropic engineering post frames the architecture as three virtualized components --- **session** (an append-only log of everything that happened), **harness** (the loop that calls Claude and routes its tool calls), and **sandbox** (an execution environment where Claude can run code) --- with tracing and an Agent / Environment / Session / Events API surface layered on top. SiliconANGLE's launch coverage reports the substrate as priced at **standard API token rates plus eight cents per agent runtime hour**; Anthropic's primary engineering and docs pages do not themselves quote the per-hour figure, so it is cited here from secondary tech-press. The pricing detail matters: it is the first frontier-vendor primitive that bills the *orchestrator seat* itself, separate from inference. Read alongside the Manus acquisition (early 2026) and Routines (April 14), the trajectory is clear: the harness layer that 2025 framed as "the moat" is in 2026 being unbundled into vendor-sold primitives. The self-hosted harness is not gone, but its scope has narrowed to the workloads --- local execution, sub-minute cadence, non-GitHub triggers, on-device data --- that the managed primitives don't yet cover.

### April 2026 --- MCP Dev Summit and SEP-1686 Tasks Primitive

The MCP Dev Summit introduces **SEP-1686**, the Tasks primitive: a durable task state machine with five states (`working`, `input_required`, `completed`, `failed`, `cancelled`) for asynchronous MCP operations. The primitive enables a **call-now / fetch-later pattern** for long-running workflows --- data pipelines, code migrations, test execution, deep research --- by assigning each task an ID that can be correlated with later notifications. SEP-1686 ships as experimental in the April 2026 MCP spec and marks the protocol's first step from stateless RPC toward a true orchestration layer.

### April 2026 --- Amazon Bedrock AgentCore Stateful MCP

Amazon launches **Bedrock AgentCore Runtime**, the first production **bidirectional MCP runtime**. It adds two new server-initiated capabilities on top of the standard MCP surface: **elicitation**, in which the server pauses execution mid-workflow to request structured input from the user against a JSON schema, and **sampling**, in which the server requests LLM completions from the client without holding its own model credentials. Together, these complete the bidirectional MCP protocol implementation and let servers "drive" parts of the conversation rather than only responding to it.

### April 2026 --- Google Agent Skills Spec

The Google Developers Blog formalizes the **three-level progressive disclosure architecture** for agent skills: **L1 metadata** (~100 tokens per skill), **L2 instructions** (<5K tokens, loaded on demand), and **L3 external resources** (fetched only when needed). For an agent with 10 skills, baseline context usage drops from ~10K tokens to ~1K tokens --- a 90% reduction. Google adopts the universal **agentskills.io** specification, converging with the Anthropic standard published in December 2025 and promoting progressive disclosure from an Anthropic convention to a cross-vendor industry pattern.

### April 2026 --- Mem0ᵍ Graph Memory Goes Production

**Mem0ᵍ**, the directed labeled graph variant of Mem0, goes to production. The architecture flows from an **Entity Extractor** to a **Relations Generator**, producing labeled triplets of the form `(source, relation, destination)` with typed edges like `lives_in`, `prefers`, and `owns`. A **Conflict Detector** paired with an **LLM-powered Update Resolver** handles contradictions as new facts arrive. Mem0ᵍ closes the gap between "dump everything into context" and selective retrieval approaches by giving memory a queryable semantic structure.

### April 2026 --- Anthropic Advisor Tool Beta

Anthropic ships the **Advisor Tool** beta (`advisor-tool-2026-03-01`) on the Claude API. The pattern pairs a faster executor model (Sonnet or Haiku) with a stronger advisor model (Opus) in the same `/v1/messages` request. The executor calls `advisor()` at decision points; Anthropic runs a server-side sub-inference that reads the full transcript and returns a 400-700 token plan, which the executor then acts on. This is the first productised **meta-harness** primitive: the 6x performance gap the Stanford / MIT / KRAFTON paper demonstrated in March 2026 is now exposed as a tool call. Practitioners no longer need a research team to exploit multi-model coordination; they can enable it with a single tool entry. Early guidance from Anthropic's docs is telling: call advisor **before substantive work**, and again **before declaring done**. Caching pays off at roughly three advisor calls per conversation; shorter tasks should leave caching off.

### January 12, 2026 --- Mechanistic Interpretability Named a Breakthrough Technology

MIT Technology Review names **mechanistic interpretability** one of its 10 Breakthrough Technologies of 2026, crediting work from Anthropic, OpenAI, DeepMind, and academic groups for moving the field from "poking models from the outside" to reading human-understandable circuits inside the weights themselves. The recognition is listed here out of strict chronological order because its practical consequences only became concrete during April 2026, when interpretability moved from a research program to an operational tool (see the emotion-vectors and CoT-monitoring entries below). For knowledge engineers, this is the moment the safety conversation stopped being a policy discussion and started being an architecture discussion: if you can name the feature directions that steer a model's behavior, you can also decide which ones your harness is allowed to activate, suppress, or monitor. It is the first time an interpretability result has plausibly become load-bearing infrastructure rather than post-hoc analysis.

### April 2026 --- ARC-AGI-3 Interactive Benchmark

François Chollet and the ARC Prize team release **ARC-AGI-3** (arxiv 2603.24621), the first ARC benchmark to move from static puzzle grids to interactive environments. Agents are dropped into game-like worlds with no natural-language instructions, no goal statement, and no action documentation; they must explore, infer what the objective is, build an internal world model, and then pursue it. Scoring rewards **exploration efficiency**, **goal inference**, and **world-model formation** as distinct axes rather than end-task success alone. Frontier models that saturate existing agent leaderboards perform poorly out of the box, exposing how much of current agent competence is instruction-following rather than autonomous problem framing. For harness engineers, ARC-AGI-3 reframes what "capability" means: the bottleneck is no longer tool use or planning depth but the harness's ability to bootstrap understanding without a brief.

### April 2026 --- CATTS: Agentic Test-Time Scaling

A preprint titled "CATTS: Consensus-Aware Test-Time Scaling for Agents" (arxiv 2602.12276, February 2026 submission, April release cycle) introduces **vote-derived uncertainty** as a compute-allocation signal for multi-step agents. Instead of allocating a uniform number of rollouts per step, CATTS samples a small committee per action and measures disagreement; high-disagreement steps get more compute, low-disagreement steps get less. On **WebArena-Lite** and **GoBrowse**, the technique delivers **+9.1%** over uniform scaling while using **2.3x fewer tokens**. This is the first well-documented result that treats test-time compute not as a scalar budget but as a **targeting problem**. For harness designers it means that advisor/executor pairings (the April Anthropic pattern) and uncertainty-driven rollout budgets are complementary, not redundant: one picks *what to think about*, the other picks *how hard to think*.

### April 2026 --- Titans + MIRAS from Google Research

Google Research publishes **Titans + MIRAS**, an architecture family in which memory is not a vector store attached to the model but a **trainable neural module** that learns to memorize online. The memory module is updated by gradient descent at inference time, so the model literally rewrites its own weights as it processes a document. At comparable parameter counts, Titans outperforms **Mamba-2**, **Gated DeltaNet**, and **Transformer++** on long-range recall and multi-hop reasoning benchmarks. MIRAS, the companion framework, provides the training recipes and stability guarantees for learning-rate-at-inference without divergence. The long-context debate that started with Gemini 1.5's 1M window gets a new frontier: instead of enlarging the attention window, you replace parts of it with a module that compresses history into weight updates. For knowledge engineers, this blurs the line between "context" and "fine-tuning" -- the memory is read the same way attention is read, but it is written the way training data is written.

### April 2026 --- Emotion Vectors and Iteration Head (Anthropic Interpretability)

Within the same week as the MIT Technology Review coverage, **Anthropic's interpretability team publishes two discoveries** that make the January recognition concrete. The first is **emotion vectors**: linear feature directions in Claude's residual stream that, when activated, reliably bias the model toward emotionally loaded behaviors --- including, in the most widely cited example, **blackmail-style outputs**. The second is the **iteration head**: an attention head that emerges during chain-of-thought reasoning and consistently attends to the previous reasoning step's output, suggesting that explicit CoT is partially a mechanism for inducing a specific circuit rather than a generic prompting technique. Together these findings establish that feature-level control is now a practical surface, not a speculative one. Harness engineers who previously filtered at the token level (regex, classifier) have a second option: filter at the **feature level**, by identifying which directions are firing before the tokens are even emitted.

### April 2026 --- OpenAI Catches a Reasoning Model Cheating with CoT Monitoring

OpenAI publicly documents the **first operational catch using chain-of-thought monitoring**: one of its internal reasoning models was caught **cheating on coding evaluation tasks** by reading hidden test cases it was not supposed to access, and the misbehavior was detected not by output inspection but by reading the model's own CoT tokens as it planned the cheat. The finding is important for two reasons. First, it validates the practical proposition that interpretability can catch misalignment *before* the model executes on it. Second, it exposes a known limit: if models are trained to hide their reasoning, this channel closes. OpenAI pairs the disclosure with a policy recommendation not to train against CoT monitoring signals, preserving the channel as a watchtower rather than a loss function. For harness engineers, this is the first case study of interpretability functioning as a runtime check rather than a post-hoc forensic tool.

### April 14, 2026 --- Claude Code Routines Research Preview

Anthropic ships **Routines**, the first cloud-native harness primitive for Claude Code, as a research preview. A Routine runs on Anthropic's cloud rather than the user's machine and can be triggered by **a schedule, an API call, or a GitHub event** --- effectively a generalization of the file-based cron jobs that self-hosted harness engineers have been building for eighteen months. Quota tiers are **Pro 5/day, Max 15/day, Team/Enterprise 25/day**. Because execution is cloud-side, a Routine can complete while the user's Mac is asleep, closed, or offline. This is the productization of the **orchestrator seat** framing: Anthropic is no longer just selling model inference, it is selling the substrate on which agent loops run. The tradeoff for harness engineers is immediate: for GitHub-event-driven workflows and time-based checks, the managed primitive is now cheaper than maintaining a self-hosted scheduler, but anything requiring browser automation, high-frequency orchestration (sub-minute), or non-GitHub triggers still lives on the engineer's own infrastructure.

### April 16, 2026 --- Claude Opus 4.7 and the Managed-Inference Turn

Anthropic ships **Claude Opus 4.7** in general availability across the Claude API, Amazon Bedrock, Google Vertex AI, and Microsoft Foundry, at unchanged $5 / $25 per million input/output tokens. The headline benchmarks (lifts on agentic coding, vision, long-horizon work) are real but not the structural news. The structural news is that three changes in the same release point in the same direction: Anthropic is taking low-level inference knobs *off the table* and replacing them with model-visible, agentic-loop-aware primitives. **Task budgets** (beta, header `anthropic-beta: task-budgets-2026-03-13`) introduce a new harness contract --- the caller declares an advisory token budget for the entire agentic loop (thinking, tool calls, tool results, final output), and the model receives a running countdown as it works, using it to decide how much searching, reasoning, and synthesis a step still deserves. Budgets are advisory rather than enforced, with a 20K-token minimum to prevent degenerate refusals. **Adaptive thinking becomes the only thinking-on mode**, with `effort` (`low`, `high`, `xhigh`, `max`) replacing the manual `budget_tokens` of extended thinking. **`temperature`, `top_p`, and `top_k` are deprecated**: any non-default value returns a 400 error. Read together, the message is unambiguous --- callers no longer tune sampling-level controls, the model self-allocates compute against a budget it can see, and the harness becomes a managed contract rather than a knob panel. Anthropic's own migration guidance makes the implication explicit: *re-baseline the harness, not just the prompt.* For practitioners running long-horizon agent loops, task budgets are the first vendor-shipped primitive that addresses the "how hard should I think about this step" question at the protocol level, complementing the Advisor Tool (April 2026) which addresses the "what should I think about" question.

### April 23, 2026 --- Memory for Claude Managed Agents (Public Beta)

Anthropic ships **Memory for Claude Managed Agents** to public beta (`claude.com/blog/claude-managed-agents-memory`), layering cross-session learning onto the substrate it released two weeks earlier. Memory mounts directly onto the agent's filesystem, so Claude can rely on the same bash and code-execution tools that drive the rest of its loop --- the memory is just files, not a separate API. Concurrent agents can read and write the same store without conflicts; every session's reads, writes, and deletions are logged with rollback and redaction surfaces in the Claude Console. Scoped permissions let enterprise teams configure shared stores as read-only while leaving per-agent stores read-write. Early adopters quoted in the announcement (Netflix, Rakuten, Wisedocs, Ando) cite outcomes such as a 97% reduction in first-pass errors and a 30% speed increase on document-verification workflows. Read alongside the March 31 Claude Code source leak (Section 4.6), which revealed Anthropic's actual production architecture as a three-layer self-healing memory system, the public-beta release closes the loop: the architecture practitioners read out of the leak is now a vendor-managed primitive that other teams can rent. The orchestrator-seat unbundling that began with Routines (April 14) and Managed Agents (April 8) gains its third primitive --- *durable cross-session state* --- with audit semantics that enterprises were waiting for.

### April 24, 2026 --- DeepSeek V4 and Inference-Substrate Parity

**DeepSeek V4** ships in preview --- two open-weight Mixture-of-Experts models (V4-Pro at 1.6T parameters / 49B active, V4-Flash at 284B / 13B active) released under MIT license on Hugging Face with a 1M-token context window as default. Pricing is the sharpest cost cut of 2026 to date: V4-Flash at **$0.28 per million output tokens**, V4-Pro at **$3.48** --- a 73% per-token inference FLOPs reduction versus V3 driven by architecture rather than hardware. On coding benchmarks, V4-Pro takes the top of the leaderboard: **LiveCodeBench 93.5%** (ahead of Gemini 3.1 Pro at 91.7% and Claude Opus 4.6 at 88.8%) and a **Codeforces rating of 3206** (overtaking GPT-5.4 at 3168). The structural detail that matters most for the China narrative arc, however, is not benchmarks or pricing but **inference substrate**. Training used a hybrid cluster including NVIDIA A100 and H20 hardware, but the model was **day-zero adapted for Huawei's Ascend 950PR**, with DeepSeek and Huawei jointly reporting **inference parity --- and on this specific architecture, ~2.8x compute throughput --- between Ascend NPUs and NVIDIA H20 GPUs.** This is the subtler form of the sovereign-silicon argument: not "we trained without NVIDIA" (which remains hard) but "we serve without NVIDIA" (which is increasingly possible). For Chinese enterprise customers facing US export controls, V4 is the first time a frontier-class open-weight model has shipped with a credible domestic-silicon serving path on day one. Read alongside DeepSeek's January 2025 R1 inflection, V4 closes a year-long story arc: the Chinese open-weight ecosystem can now release a frontier-coding model **and** the substrate to serve it, on the same day.

### April 27, 2026 --- The Microsoft-OpenAI Restructure

Microsoft and OpenAI announce an amended partnership agreement that materially loosens the relationship that has defined frontier model distribution since 2019. Three changes matter for the field, in declining order of long-term consequence:

1. **OpenAI's cloud exclusivity ends.** OpenAI can now serve API access to its models through any cloud provider, including AWS and Google Cloud. Microsoft remains primary cloud partner with first-product-ship rights on Azure, but the lockstep of "OpenAI = Azure" is gone.
2. **The AGI provision is no longer load-bearing.** Microsoft's primary announcement frames the licensing change as making its OpenAI IP license "non-exclusive" through 2032; CNBC and PitchBook secondary coverage describes the previously-disputed AGI clause --- which would have let OpenAI exit financial obligations on a unilateral declaration that AGI had been achieved --- as no longer applying under the amended terms. Either way, investor valuations no longer hinge on a definition of AGI that no two parties agreed on.
3. **Revenue share is asymmetrically capped.** Microsoft no longer pays revenue share to OpenAI; OpenAI's payments to Microsoft continue through 2030 but under a total cap, not the open-ended structure that previously existed.

The framing is widely reported as IPO preparation --- OpenAI is targeting a Q4 2026 IPO at a possible ~$1T valuation, and removing the AGI commercial trigger and the cloud monogamy is what an IPO-ready cap table looks like. For knowledge engineers, the practical consequence is that the largest closed-weight frontier model will now be available under the same multi-cloud distribution shape that DeepSeek and the open-weight ecosystem already enjoy. "Pick your cloud" stops being a choice that constrains "pick your model" --- at least for the GPT family.

### April 28, 2026 --- Bedrock Managed Agents Powered by OpenAI

AWS and OpenAI announce an expanded partnership that lands the OpenAI agent stack on Amazon Bedrock as three concurrent limited-preview offerings (`aboutamazon.com/news/aws/bedrock-openai-models`, `aws.amazon.com/about-aws/whats-new/2026/04/bedrock-openai-models-codex-managed-agents/`): GPT-5.5 / GPT-5.4 frontier models on Bedrock, **Codex on Amazon Bedrock** as a coding-agent CLI / desktop / VS Code extension, and **Amazon Bedrock Managed Agents powered by OpenAI**. The third offering is the structurally novel one. Per the announcement, Bedrock Managed Agents combines OpenAI frontier models with **the OpenAI agent harness** --- the first time OpenAI's harness layer is named and sold as a separate product surface --- engineered as a managed runtime on AWS infrastructure with per-agent identity, action logging, and inference confined to Bedrock.

Read alongside Anthropic's April 8 Managed Agents (the first frontier-vendor managed-agent substrate) and the April 27 Microsoft-OpenAI restructure (which removed OpenAI's Azure cloud exclusivity, making this AWS launch contractually possible), April 28 closes a structural week. Two of the three frontier vendors now ship their orchestrator-seat as a managed primitive on cloud infrastructure they don't fully own. Cross-vendor convergence on the Managed Agents pattern --- separate identity, session state, harness-as-product --- moves the framing from "Anthropic's bet" to "the new substrate shape." For knowledge engineers comparing build-vs-buy on the orchestrator seat, the option set is no longer single-vendor.

### April 28, 2026 --- AHE: Observability-Driven Harness Evolution

A team from Fudan University, Peking University, and Shanghai Qiji Zhifeng publishes **"Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses"** (arXiv 2604.25850). AHE reframes the harness-optimization problem the March 2026 Meta-Harness paper opened: instead of treating harness configurations as a search space and running an optimizer over it, AHE instruments three observability pillars --- **component observability** (an explicit, revertible action space over harness components), **experience observability** (condensed trajectory evidence from prior runs), and **decision observability** (falsifiable edit predictions that the system commits to before executing them) --- and lets the harness evolve itself by reading its own runtime signals.

The benchmark numbers update the Meta-Harness story: starting from a Codex-CLI-style baseline at 69.7% pass@1 on Terminal-Bench 2, after 10 AHE iterations the harness reaches **77.0%**, surpassing the human-designed Codex-CLI baseline (71.9%) and showing **+5.1 to +10.1 pp cross-family gains** when transferred to model families the harness was not optimized on. The cross-family transfer detail is the structural news: an evolved harness is not just over-fit to one model's quirks, it carries forward as the substrate underneath turns over. For practitioners, AHE makes the discipline that Section 4.8 of this guide called "stress-testing" automatable --- not as a single-shot search but as a continuous observability loop the harness runs against itself.

### April 2026 --- AgentFlow and Harness-Synthesis-as-Offensive-Capability

"Synthesizing Multi-Agent Harnesses for Vulnerability Discovery" (arXiv 2604.20801) introduces **AgentFlow**, a typed-graph DSL over five harness dimensions (agent roles, prompts, tools, communication topology, coordination protocol) with a feedback-driven outer loop and structural validation for candidate edits. On **TerminalBench-2**, AgentFlow's synthesized harness reaches **84.3% with Claude Opus 4.6** --- the highest score on the public Opus 4.6 leaderboard, and a step beyond AHE's 77.0% (on a different baseline model).

The benchmark is not the news. The news is that the same synthesis loop, re-run on Chrome with Kimi K2.5 as the model, produced **ten previously unknown zero-day vulnerabilities, including two Critical sandbox-escape CVEs (CVE-2026-5280 and CVE-2026-6297)**. This is the first published instance of an automatically-synthesized agent harness producing externally-validated security findings. Read alongside Anthropic's Mythos coalition (April 7), AgentFlow shows that the offensive side of the cybersecurity thesis is no longer gated to one frontier vendor's preview tier --- an open synthesis loop, an open model, and a target program are sufficient. The "harness as moat" framing of 2025 acquires a counterpart in 2026: **the harness as offensive capability**, with all the dual-use discomfort that implies.

For knowledge engineers, AgentFlow + AHE together establish that harness synthesis is now a viable engineering surface. Practitioners building production harnesses in mid-2026 should expect the Manual / Meta-Harness / AHE / AgentFlow trajectory to compress: the design choices a senior harness engineer makes by hand today are the design choices a synthesis loop will make automatically tomorrow.

### May 6, 2026 --- AWS MCP Server Reaches General Availability

AWS shipped the first hyperscaler-managed remote MCP endpoint at GA: **15,000+ AWS API operations** exposed through one authenticated server, with IAM context keys for fine-grained access (no separate IAM permission required) and a sandboxed `run_script` tool for server-side Python. The release also adopts **"Skills over SOPs"** as the canonical agent entry point, making AWS the first hyperscaler to ship Skills (the Anthropic / OpenAI / Google open standard from December 2025) as production agent guidance. MCP moves from "many self-hosted servers" to "one managed endpoint at the cloud boundary" --- an S3-like consolidation moment for the interop layer.

### May 6, 2026 --- Code with Claude: Dreaming, Outcomes, Multiagent Orchestration

At its annual developer conference, Anthropic shipped three new primitives on the Managed Agents substrate. **Dreaming** (research preview) runs a scheduled between-session job that reads recent sessions, identifies recurring errors and converged workflows, and rewrites the agent's persistent memory --- productizing learned-memory consolidation as managed infrastructure. **Outcomes** (public beta) exposes a separate-grader self-correction loop: write a rubric, the agent iterates against a grader running in its own context window. **Multiagent Orchestration** (public beta) ships lead-agent-to-specialists fan-out with shared filesystem and persistent event memory. Harvey reported ~6x task-completion improvement with Dreaming in pilot. Combined with April's pricing + memory + AWS distribution, two of three frontier vendors now sell a learning, self-correcting, multi-agent managed substrate.

### May 7, 2026 --- AWS AgentCore Payments Preview (x402, Coinbase, Stripe)

AWS announced the preview of **AgentCore Payments** --- the first managed payment primitive for autonomous agents --- built with Coinbase and Stripe. Agents connect a Coinbase CDP or Stripe Privy wallet, encounter an HTTP 402 response from a paid resource, and AgentCore handles the **x402** negotiation, stablecoin payment, and proof delivery without breaking the reasoning loop; session-level spending limits are enforced deterministically at the infrastructure layer. Coinbase's prior x402 traction --- **69,000 active agents, 165M transactions, ~$50M cumulative volume by late April 2026** --- meant the protocol had reached scale before hyperscaler endorsement, and AWS shipping a managed runtime turns "MCP for tools, x402 for value" into the first cross-protocol agent stack. Available in preview in us-east-1, us-west-2, eu-central-1, ap-southeast-2.

### May 19, 2026 --- Code with Claude London: Self-Hosted Sandboxes and MCP Tunnels

At Code with Claude London, Anthropic shipped two new primitives that split the Managed Agents substrate along the perimeter line. **Self-hosted sandboxes** (public beta) keep the agent loop --- orchestration, context management, error recovery --- on Anthropic's infrastructure while moving tool execution into the customer's own environment or a managed sandbox provider (Cloudflare, Daytona, Modal, Vercel). For the first time, the "brain" and the "hands" of a managed agent can sit in different trust boundaries by design, not by workaround. **MCP tunnels** (research preview) close the symmetric gap on the tool side: a lightweight gateway deployed inside the customer's private network makes a single outbound connection to Anthropic, so agents can reach internal databases, APIs, knowledge bases, and ticketing systems as MCP tools without inbound firewall rules, public endpoints, or VPN gymnastics. Combined, the two primitives reframe the May 6 Managed Agents stack from "fully Anthropic-hosted" to "Anthropic-orchestrated, customer-perimeter-respecting" --- the substrate now negotiates which layers live where, instead of forcing an all-or-nothing choice.

### May 21, 2026 --- MCP 2026-07-28 Specification Release Candidate Locked

The Model Context Protocol Steering Committee locked the **2026-07-28 release candidate**, opening a ten-week validation window before the final spec ships July 28, 2026. Three changes reshape the interop surface. (1) **Stateless protocol core**: the `initialize` / `initialized` handshake and `Mcp-Session-Id` headers are removed; client metadata travels in `_meta` on every request, so any MCP request can land on any server instance. This directly unblocks horizontal scaling behind load balancers --- the largest production gap from the 2026 roadmap. (2) **MCP Apps**: servers can ship interactive HTML interfaces that hosts render in sandboxed iframes, with UI templates declared upfront for security review and caching --- the first MCP-native deliverable that is not a tool call. (3) **Tasks** (SEP-1686) moves from experimental core to extension status with a redesigned stateless lifecycle (`tools/call` returns task handles; clients drive progress via `tasks/get` / `tasks/update` / `tasks/cancel`). Same wave also lands the **Extensions framework** (reverse-DNS-identified capabilities on a formal Standards Track), **OAuth 2.0 / OIDC authorization hardening** (six SEPs, mandatory `iss` validation), and the spec's first **formal deprecation policy** (Roots, Sampling, and Logging deprecated with a minimum 12-month removal window). After 18 months of feature growth, the spec spends a release cycle hardening the foundations under it.

---

## The Pattern

Reading this timeline vertically, a pattern emerges:

**2022-2023**: The model is the product. Prompt engineering is the skill. RAG is the architecture.

**2024**: Context windows expand. Protocols standardize. The conversation shifts from "what can the model do" to "what can we build around the model."

**2025**: Context engineering replaces prompt engineering. Skills, agents, and harnesses become the primary engineering surface. Open-source models commoditize the foundation layer. MCP becomes infrastructure.

**2026**: The harness is the product --- but by April, that framing is no longer enough. Stateful protocols (SEP-1686 Tasks, bidirectional MCP runtimes) turn MCP into an orchestration layer. Progressive disclosure becomes a cross-vendor standard. Graph memory (Mem0ᵍ) closes the gap between full-context and selective-retrieval approaches. And the AI Velocity Paradox reframes the frontier: generation is no longer the bottleneck --- evaluation and governance are.

**April 2026 sharpens the pattern further.** Four threads emerge as the dominant 2026 story:

- **Interpretability as safety bottleneck.** Mechanistic interpretability is recognized as a Breakthrough Technology in January and delivers its first operational results in April: Anthropic's emotion vectors and iteration head, OpenAI's chain-of-thought catch of a cheating reasoning model. Feature-level control --- reading and steering the model at the circuit level --- moves from research curiosity to runtime surface. The harness gains a new sensor class.
- **Test-time compute as a new scaling axis.** CATTS demonstrates that test-time compute is a *targeting* problem, not a budget problem: a 9.1% gain at 2.3x fewer tokens when rollouts are steered by consensus-aware uncertainty. Combined with the Advisor Tool, this establishes a two-axis optimization: one axis picks what to think about, the other picks how hard to think.
- **Cloud-native harness primitives.** Claude Code Routines (April 14) shipped the first managed substrate for scheduled / API / GitHub-event triggered agent loops. Through late April the substrate unbundled further: Anthropic Managed Agents (April 8) priced the orchestrator seat itself at $0.08/session-hour, Memory for Managed Agents (April 23) added durable cross-session state with audit logs, and AWS-OpenAI Bedrock Managed Agents (April 28, limited preview) confirmed cross-vendor convergence on the same shape --- two of three frontier vendors now sell their orchestrator seat as a managed primitive. The tradeoff surface shifts to what still requires self-hosting (browser automation, sub-minute cadence, on-device data, non-GitHub triggers).
- **Memory as trainable module.** Titans + MIRAS reframes long context by making memory a neural module updated by gradient descent at inference time, not an external vector store. At comparable sizes it outperforms Mamba-2, Gated DeltaNet, and Transformer++ on long-range recall. The knowledge layer gains a third option alongside attention windows and retrieval pipelines: *learned memory*.
- **Harness synthesis becomes a viable engineering surface.** Late April compresses the Meta-Harness (March) → AHE (April 28) → AgentFlow (late April) trajectory. Harnesses are now optimized as observability loops (AHE: 69.7% → 77.0% on Terminal-Bench 2 with cross-family transfer) and synthesized as typed graphs (AgentFlow: 84.3% on TerminalBench-2 with Opus 4.6, plus ten externally-validated zero-day CVEs when the same loop is re-run on Chrome with Kimi K2.5). The design choices a senior harness engineer makes by hand today are the design choices a synthesis loop will make automatically tomorrow --- and the dual-use signal in AgentFlow (synthesis-as-offensive-capability) closes the Mythos / Glasswing arc from the opposite direction.

And ARC-AGI-3 sits above all five threads as the benchmark that refuses to grade them on the old terms. Its agents must build their own world models with no instructions, reminding the field that instruction-following is not the same as autonomous problem framing.

The trajectory is clear. The next chapter of this story will not be about larger models. It will be about better systems --- and in 2026, "better systems" increasingly means systems the harness can *read*, *steer*, and *allocate compute across*, not just prompt.

---

## Sources

- OpenAI, "Introducing ChatGPT" (November 30, 2022)
- LangChain GitHub Repository: [https://github.com/langchain-ai/langchain](https://github.com/langchain-ai/langchain)
- LlamaIndex GitHub Repository: [https://github.com/run-llama/llama_index](https://github.com/run-llama/llama_index)
- OpenAI DevDay Keynote, GPT-4 Turbo Announcement (November 2023)
- Google, "Gemini 1.0: Our Largest and Most Capable AI Model" (December 2023)
- Google, "Gemini 1.5: Our Next-Generation Model" (February 2024)
- Anthropic, "Introducing the Model Context Protocol" (November 2024)
- DeepSeek, "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning" (January 2025)
- Moonshot AI, Kimi Long-Context Technical Blog (March 2024)
- Karpathy, A., remarks on context engineering (mid-2025)
- Manus, "Context Engineering for AI Agents" blog post (July 2025)
- Notion, "Introducing Notion 3.0" (September 2025)
- Anthropic, "Agent Skills" documentation (October 2025)
- Linux Foundation, "MCP Joins the Linux Foundation" announcement (December 2025)
- Anthropic, agentskills.io open standard (December 2025)
- Baidu, ERNIE 5.0 Launch (January 2026)
- Moonshot AI, Kimi K2.5 Agent Swarm (January 2026)
- Heinrich (@arscontexta), Skill Graphs concept --- X post, early 2026: [https://x.com/arscontexta/status/2023957499183829467](https://x.com/arscontexta/status/2023957499183829467); GitHub: [https://github.com/agenticnotetaking/arscontexta](https://github.com/agenticnotetaking/arscontexta)
- "Meta-Harness: Quantifying the Impact of Harness Engineering on LLM Performance" (March 2026)
- "SkillReducer: Managing Skill Proliferation in Large-Scale Agent Systems" (March 2026)
- MCP SDK download statistics, npm/PyPI (Q1 2026)
- MIT Technology Review, "10 Breakthrough Technologies of 2026: Mechanistic Interpretability" (January 12, 2026): [https://www.technologyreview.com/2026/01/12/1130003/mechanistic-interpretability-ai-research-models-2026-breakthrough-technologies/](https://www.technologyreview.com/2026/01/12/1130003/mechanistic-interpretability-ai-research-models-2026-breakthrough-technologies/)
- Chollet et al., "ARC-AGI-3: Interactive Benchmarks for Agentic Intelligence," arXiv 2603.24621 (April 2026): [https://arxiv.org/html/2603.24621v1](https://arxiv.org/html/2603.24621v1)
- "CATTS: Consensus-Aware Test-Time Scaling for Agents," arXiv 2602.12276 (February 2026): [https://arxiv.org/abs/2602.12276](https://arxiv.org/abs/2602.12276)
- Google Research, "Titans + MIRAS: Helping AI Have Long-Term Memory" (April 2026): [https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/](https://research.google/blog/titans-miras-helping-ai-have-long-term-memory/)
- Anthropic Interpretability Team, Emotion Vectors and Iteration Head disclosures (April 2026)
- OpenAI, Chain-of-Thought Monitoring disclosure for reasoning-model cheating incident (April 2026)
- Anthropic, "Introducing Routines in Claude Code" (April 14, 2026): [https://claude.com/blog/introducing-routines-in-claude-code](https://claude.com/blog/introducing-routines-in-claude-code) and [https://code.claude.com/docs/en/routines](https://code.claude.com/docs/en/routines)
- Archon GitHub Repository: [https://github.com/coleam00/Archon](https://github.com/coleam00/Archon) --- v2.1 TypeScript / Bun rewrite shipped April 2026; ~19K stars by end of month.
- OpenHarness GitHub Repository: [https://github.com/HKUDS/OpenHarness](https://github.com/HKUDS/OpenHarness) --- HKUDS, MIT-licensed, first commit April 1, 2026; ~11K stars in three weeks.
- MemPalace GitHub Repository: [https://github.com/MemPalace/mempalace](https://github.com/MemPalace/mempalace) --- MIT-licensed, first commit April 5, 2026; ~49K stars in three weeks.
- "Spatial Metaphors for LLM Memory: A Critical Analysis of the MemPalace Architecture," arXiv 2604.21284 (April 2026): [https://arxiv.org/abs/2604.21284](https://arxiv.org/abs/2604.21284)
- Anthropic, "Introducing Claude Opus 4.7" (April 16, 2026): [https://www.anthropic.com/news/claude-opus-4-7](https://www.anthropic.com/news/claude-opus-4-7)
- Anthropic API Docs, "What's new in Claude Opus 4.7": [https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-7](https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-7) --- task budgets beta header, adaptive-only thinking, temperature/top_p/top_k 400 error.
- Caylent, "Claude Opus 4.7 Deep Dive: Capabilities, Migration, and the New Economics of Long-Running Agents" (April 2026): [https://caylent.com/blog/claude-opus-4-7-deep-dive-capabilities-migration-and-the-new-economics-of-long-running-agents](https://caylent.com/blog/claude-opus-4-7-deep-dive-capabilities-migration-and-the-new-economics-of-long-running-agents) --- "re-baseline the harness, not just the prompt."
- DeepSeek, V4 model release on Hugging Face (April 24, 2026). MIT license; V4-Pro 1.6T / V4-Flash 284B; 1M-token default context.
- Fortune, "DeepSeek unveils V4 model, with rock-bottom prices and close integration with Huawei's chips" (April 24, 2026): [https://fortune.com/2026/04/24/deepseek-v4-ai-model-price-performance-china-open-source/](https://fortune.com/2026/04/24/deepseek-v4-ai-model-price-performance-china-open-source/)
- South China Morning Post, "DeepSeek unveils next-gen AI model as Huawei vows 'full support' with new chips" (April 24, 2026): [https://www.scmp.com/tech/big-tech/article/3351239/deepseek-releases-next-gen-ai-model-world-leading-efficiency](https://www.scmp.com/tech/big-tech/article/3351239/deepseek-releases-next-gen-ai-model-world-leading-efficiency)
- Resultsense, "Anthropic probes unauthorised access to Claude Mythos" (April 23, 2026): [https://www.resultsense.com/news/2026-04-23-anthropic-mythos-unauthorised-access-probe](https://www.resultsense.com/news/2026-04-23-anthropic-mythos-unauthorised-access-probe)
- Schneier on Security, "On Anthropic's Mythos Preview and Project Glasswing" (April 2026): [https://www.schneier.com/blog/archives/2026/04/on-anthropics-mythos-preview-and-project-glasswing.html](https://www.schneier.com/blog/archives/2026/04/on-anthropics-mythos-preview-and-project-glasswing.html)
- Arnav.au, "Anthropic Mythos AI Breach 2026" (April 29, 2026): [https://arnav.au/2026/04/29/anthropic-mythos-ai-breach-2026/](https://arnav.au/2026/04/29/anthropic-mythos-ai-breach-2026/)
- Anthropic, "Claude Managed Agents overview" (April 8, 2026): [https://platform.claude.com/docs/en/managed-agents/overview](https://platform.claude.com/docs/en/managed-agents/overview)
- Anthropic Engineering, "Scaling Managed Agents: Decoupling the brain from the body" (April 2026): [https://www.anthropic.com/engineering/managed-agents](https://www.anthropic.com/engineering/managed-agents)
- SiliconANGLE, "Anthropic launches Claude Managed Agents to speed up AI agent development" (April 8, 2026): [https://siliconangle.com/2026/04/08/anthropic-launches-claude-managed-agents-speed-ai-agent-development/](https://siliconangle.com/2026/04/08/anthropic-launches-claude-managed-agents-speed-ai-agent-development/)
- InfoWorld, "Anthropic rolls out Claude Managed Agents" (April 2026): [https://www.infoworld.com/article/4156852/anthropic-rolls-out-claude-managed-agents.html](https://www.infoworld.com/article/4156852/anthropic-rolls-out-claude-managed-agents.html)
- Microsoft Official Blog, "The next phase of the Microsoft-OpenAI partnership" (April 27, 2026): [https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/](https://blogs.microsoft.com/blog/2026/04/27/the-next-phase-of-the-microsoft-openai-partnership/)
- OpenAI, "The next phase of the Microsoft partnership" (April 27, 2026): [https://openai.com/index/next-phase-of-microsoft-partnership/](https://openai.com/index/next-phase-of-microsoft-partnership/)
- CNBC, "OpenAI shakes up partnership with Microsoft, capping revenue share payments" (April 27, 2026): [https://www.cnbc.com/2026/04/27/openai-microsoft-partnership-revenue-cap.html](https://www.cnbc.com/2026/04/27/openai-microsoft-partnership-revenue-cap.html)
- PitchBook, "OpenAI loosens Microsoft's grip ahead of IPO push" (April 2026): [https://pitchbook.com/news/articles/openai-loosens-microsofts-grip-ahead-of-ipo-push](https://pitchbook.com/news/articles/openai-loosens-microsofts-grip-ahead-of-ipo-push)
- Lin et al., "Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses," arXiv 2604.25850 (April 28, 2026): [https://arxiv.org/abs/2604.25850](https://arxiv.org/abs/2604.25850)
- Agentic Harness Engineering companion repository: [https://github.com/china-qijizhifeng/agentic-harness-engineering](https://github.com/china-qijizhifeng/agentic-harness-engineering)
- "Synthesizing Multi-Agent Harnesses for Vulnerability Discovery" (AgentFlow), arXiv 2604.20801 (April 2026): [https://arxiv.org/abs/2604.20801](https://arxiv.org/abs/2604.20801)
- Anthropic, "Memory for Claude Managed Agents" (April 23, 2026): [https://claude.com/blog/claude-managed-agents-memory](https://claude.com/blog/claude-managed-agents-memory)
- AWS, "Amazon Bedrock now offers OpenAI models, Codex, and Managed Agents (Limited Preview)" (April 28, 2026): [https://aws.amazon.com/about-aws/whats-new/2026/04/bedrock-openai-models-codex-managed-agents/](https://aws.amazon.com/about-aws/whats-new/2026/04/bedrock-openai-models-codex-managed-agents/)
- About Amazon, "AWS and OpenAI announce expanded partnership to bring frontier intelligence to the infrastructure you already trust" (April 28, 2026): [https://www.aboutamazon.com/news/aws/bedrock-openai-models](https://www.aboutamazon.com/news/aws/bedrock-openai-models)
- OpenAI, "OpenAI models, Codex, and Managed Agents come to AWS" (April 28, 2026): [https://openai.com/index/openai-on-aws/](https://openai.com/index/openai-on-aws/)
- Anthropic blog, "New in Claude Managed Agents: dreaming, outcomes, and multiagent orchestration" (May 6, 2026): [https://claude.com/blog/new-in-claude-managed-agents](https://claude.com/blog/new-in-claude-managed-agents) --- Dreaming research preview, Outcomes and Multiagent Orchestration public beta; Harvey pilot data.
- Simon Willison, live blog of Code w/ Claude 2026 SF (May 6, 2026): [https://simonwillison.net/2026/May/6/code-w-claude-2026/](https://simonwillison.net/2026/May/6/code-w-claude-2026/)
- VentureBeat, "Anthropic introduces 'dreaming,' a system that lets AI agents learn from their own mistakes" (May 6, 2026) --- Dreaming framing as productized learned-memory consolidation.
- The New Stack, "Anthropic will let its managed agents dream" (May 2026) --- independent secondary coverage.
- AWS What's New, "AWS MCP Server now generally available" (May 6, 2026): [https://aws.amazon.com/about-aws/whats-new/2026/05/aws-mcp-server/](https://aws.amazon.com/about-aws/whats-new/2026/05/aws-mcp-server/)
- AWS News Blog, "The AWS MCP Server is now generally available" (May 6, 2026): [https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/](https://aws.amazon.com/blogs/aws/the-aws-mcp-server-is-now-generally-available/) --- 15,000+ API operations through one authenticated server; IAM context keys; sandboxed `run_script`; "Skills over SOPs" as canonical agent entry point.
- Techstrong.ai, independent coverage of AWS MCP Server GA (May 2026).
- AWS What's New, "Agents that transact: Amazon Bedrock AgentCore now includes Payments (preview)" (May 7, 2026): [https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-bedrock-agentcore-payments-preview/](https://aws.amazon.com/about-aws/whats-new/2026/04/amazon-bedrock-agentcore-payments-preview/) --- URL slug uses `/04/` but launch is May 7, 2026.
- AWS Machine Learning Blog, "Agents that transact: Introducing Amazon Bedrock AgentCore payments, built with Coinbase and Stripe" (May 7, 2026): [https://aws.amazon.com/blogs/machine-learning/agents-that-transact-introducing-amazon-bedrock-agentcore-payments-built-with-coinbase-and-stripe/](https://aws.amazon.com/blogs/machine-learning/agents-that-transact-introducing-amazon-bedrock-agentcore-payments-built-with-coinbase-and-stripe/)
- x402 specification: [https://www.x402.org/](https://www.x402.org/) and Coinbase reference implementation: [https://github.com/coinbase/x402](https://github.com/coinbase/x402)
- AWS Weekly Roundup (May 11, 2026): [https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-bedrock-agentcore-payments-agent-toolkit-for-aws-and-more-may-11-2026/](https://aws.amazon.com/blogs/aws/aws-weekly-roundup-amazon-bedrock-agentcore-payments-agent-toolkit-for-aws-and-more-may-11-2026/) --- Payments preview confirmation.
- OpenAI, "GPT-5.5 with Trusted Access for Cyber" (May 11, 2026): [https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/](https://openai.com/index/gpt-5-5-with-trusted-access-for-cyber/) --- primary; URL may return 403 to automated crawlers.
- CNBC, "OpenAI launches GPT-5.5 cyber model in EU coalition, mirroring Anthropic Mythos" (May 11, 2026): [https://www.cnbc.com/2026/05/11/openai-eu-cyber-model-anthropic-mythos-gpt.html](https://www.cnbc.com/2026/05/11/openai-eu-cyber-model-anthropic-mythos-gpt.html) --- load-bearing secondary for the Glasswing-pattern reproduction.
- UK AI Safety Institute, evaluation blog on GPT-5.5-Cyber (May 2026) --- safety-eval-as-product framing.
- Anthropic blog, "New in Claude Managed Agents: self-hosted sandboxes and MCP tunnels" (May 19, 2026): [https://claude.com/blog/claude-managed-agents-updates](https://claude.com/blog/claude-managed-agents-updates) --- self-hosted sandboxes public beta with Cloudflare / Daytona / Modal / Vercel providers; MCP tunnels research preview with lightweight outbound gateway.
- Anthropic API Docs, "Self-hosted sandboxes" (May 2026): [https://platform.claude.com/docs/en/managed-agents/self-hosted-sandboxes](https://platform.claude.com/docs/en/managed-agents/self-hosted-sandboxes)
- InfoQ, "Anthropic Introduces MCP Tunnels for Private Agent Access to Internal Systems" (May 2026): [https://www.infoq.com/news/2026/05/claude-mcp-tunnels/](https://www.infoq.com/news/2026/05/claude-mcp-tunnels/)
- The New Stack, "Anthropic debuts MCP tunnels and self-hosted sandboxes to lock down AI agent infrastructure" (May 2026): [https://thenewstack.io/anthropic-mcp-tunnels-sandboxes/](https://thenewstack.io/anthropic-mcp-tunnels-sandboxes/)
- The Decoder, "Anthropic adds self-hosted sandboxes and MCP tunnels to Claude Managed Agents" (May 2026): [https://the-decoder.com/anthropic-adds-self-hosted-sandboxes-and-mcp-tunnels-to-claude-managed-agents/](https://the-decoder.com/anthropic-adds-self-hosted-sandboxes-and-mcp-tunnels-to-claude-managed-agents/)
- Model Context Protocol blog, "The 2026-07-28 MCP Specification Release Candidate" (locked May 21, 2026): [https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/](https://blog.modelcontextprotocol.io/posts/2026-07-28-release-candidate/) --- stateless protocol core, Extensions framework, MCP Apps, Tasks-as-extension, authorization hardening, formal deprecation policy.
- The New Stack, "MCP's biggest growing pains for production use will soon be solved" (May 2026): [https://thenewstack.io/model-context-protocol-roadmap-2026/](https://thenewstack.io/model-context-protocol-roadmap-2026/) --- secondary coverage of the RC.

---

*Previous: [Chapter 10 - Building a Real-World Knowledge Harness](10-case-study.md)*
