# Multi-Agent Collaboration

## Overview

The **Multi-Agent Collaboration** pattern uses a *team of specialized agents* to solve a complex task by decomposing it into smaller sub-tasks and coordinating how those agents communicate and share intermediate outputs.

A useful analogy: even on a single computer, we break work into **multiple processes/threads** to make complex programs easier to build and reason about. Similarly, instead of “one super employee,” we build a **team** (researcher, designer, writer, editor, etc.) where each role is optimized for a part of the workflow.

---

## Why Use This Pattern?

Single-agent systems often hit limits when tasks require:

* **Multiple distinct skills** (research, statistics, writing, design, QA)
* **Different tool access** (web search, code execution, image generation, internal catalogs)
* **Quality control** (review, fact checking, policy/compliance checks)
* **Parallelization** (subtasks can run simultaneously)

Multi-agent collaboration helps by:

* **Decomposing complexity** into manageable chunks
* **Specializing prompts + tools** per role (better outputs, less confusion)
* **Increasing robustness** (reviewers catch mistakes; fewer hallucinations)
* **Improving scalability** (reuse agents across workflows)

---

## When to Use

### ✅ Ideal Use Cases

* **Complex research & analysis**: research agent + statistician + writer + editor
* **Software development**: requirements agent + coder + tester + doc writer
* **Creative campaigns**: trend researcher + graphic designer (image tools) + copywriter + packager
* **Network triage / remediation**: multiple agents to pinpoint failures and suggest fixes
* **Customer support escalation**: frontline agent → specialist agent handoff

### ❌ When NOT to Use

* Simple tasks that a single agent can complete reliably
* Workflows that can’t be decomposed meaningfully
* Situations where tool latency/coordination overhead outweighs benefits
* Environments lacking safe orchestration, logging, and permissions

---

## Core Idea: Roles + Communication

Multi-agent systems are primarily defined by:

1. **Agent roles** (who does what)
2. **Communication pattern** (who talks to whom, and when)
3. **Tools available per agent** (what each agent can do externally)

### Example Role Breakdown (Marketing Assets)

* **Research Agent**: web search for trends + competitor analysis
* **Graphic Designer Agent**: image generation, chart creation (may use code execution)
* **Writer / Copywriter Agent**: marketing copy and narrative (often no tools needed)
* **Packaging Agent**: compiles final report into a shareable artifact
* Optional: **Manager Agent**: delegates and coordinates the above

---

## Common Collaboration Patterns

### 1) Sequential Handoffs (Linear Pipeline)

One agent completes a step, passes output to the next.

```
Research → Design → Write → Package
```

**Best for:** predictable pipelines, clear stages, easy debugging.

---

### 2) Supervisor / Manager (Hierarchical)

A manager agent delegates tasks to specialists and synthesizes results.

```
Manager
 ├─ Research Agent
 ├─ Designer Agent
 └─ Writer Agent
```

**Best for:** dynamic task planning, variable complexity, strong central control.
**Tradeoff:** manager can become a bottleneck or single point of failure.

---

### 3) Deeper Hierarchy (Multi-layer Org)

Agents can spawn or call sub-agents (e.g., research → web researcher + fact checker).

```
Manager
 ├─ Research Lead
 │   ├─ Web Researcher
 │   └─ Fact Checker
 └─ Writing Lead
     ├─ Draft Writer
     └─ Citation Checker
```

**Best for:** very complex tasks requiring layered QA.
**Tradeoff:** increased complexity and coordination overhead.

---

### 4) Parallel Processing

Multiple agents work simultaneously on independent parts, then merge outputs.

```
            ┌─ Research A (pricing)
User Task ──┼─ Research B (competitors) ──→ Merge/Synthesize → Output
            └─ Research C (trends)
```

**Best for:** speedups and coverage on decomposable tasks.
**Tradeoff:** requires good merging logic and conflict resolution.

---

### 5) Debate & Consensus

Agents with different viewpoints argue, critique, and converge on a decision.

**Best for:** evaluating options, policy-sensitive decisions, architecture tradeoffs.
**Tradeoff:** can be slow and unpredictable without guardrails.

---

### 6) All-to-All Network (Decentralized)

Any agent can message any other agent at any time.

**Best for:** exploratory, creative, “chaos-tolerant” workflows.
**Tradeoff:** outcomes are harder to predict; needs strong stopping criteria.

---

## Interrelationship Models (System Design Choices)

When designing multi-agent systems, you’ll often choose an interaction model:

* **Single Agent**: simplest, limited scope
* **Network**: decentralized peer-to-peer collaboration
* **Supervisor**: central coordinator assigns work
* **Supervisor as a Tool**: supervisor provides guidance/services rather than direct control
* **Hierarchical**: multiple supervisor layers
* **Custom**: hybrid, tailored model for domain constraints and performance goals

---

## How It Works

1. **Task decomposition**: split the user request into sub-tasks
2. **Assignment**: route each sub-task to the best-fit agent
3. **Execution**: agents use tools (if enabled) and produce intermediate outputs
4. **Aggregation**: merge outputs into a unified answer/report
5. **Quality control**: critique/review/reflection to improve correctness and style
6. **Packaging**: finalize into an artifact (Markdown/PDF/slides/etc.)

---

## Tooling: Agents as “Tools”

In tool-use systems, an LLM chooses which tool function to call.
In multi-agent systems, the coordinator can treat each **agent** as a callable capability:

* “Call ResearchAgent for trend analysis”
* “Call DesignerAgent for visual assets”
* “Call WriterAgent for the final narrative”

This is conceptually similar to function calling—except the “functions” are **LLM roles** (often with different tools enabled).

---

## Reference Implementation Pattern

### A) Linear Pipeline (Simple + Reliable)

**Flow:**

1. Research agent gathers facts (web/tools)
2. Designer creates visuals (image tools/code)
3. Writer drafts final copy
4. Packaging agent compiles report

**Strengths:** clarity, repeatability, easier testing.

---

### B) Supervisor-Orchestrated Pipeline (Flexible)

**Flow:**

1. Manager creates a plan
2. Manager delegates steps to specialists
3. Specialists return results to manager
4. Manager performs synthesis + final review

**Strengths:** adapts to changing tasks, centralized quality control.

---

## Critic–Reviewer Variant (Quality Booster)

A common robustness pattern is to separate creation from critique:

* **Creators** generate drafts (plans, code, reports)
* **Reviewers** check correctness, security, compliance, quality
* **Final agent** revises based on feedback

This often reduces hallucinations and improves output quality, especially for:

* code generation
* research writing
* logic checking
* policy/compliance alignment

---

## Practical Applications

* **Research projects**: arXiv agent + web agent + summarizer + synthesis writer
* **Engineering workflows**: architect agent + implementation agent + test agent + docs agent
* **Campaign generation**: market research + designer + copywriter + packaging
* **Ops remediation**: triage + root cause + fix recommender + change reviewer
* **Support escalation**: generalist agent → specialist agent when needed

---

## Design Considerations

### Communication & Control

* Choose a pattern that matches your tolerance for unpredictability
* Define “done” criteria (especially in network/all-to-all setups)

### Tool Access

* Not every agent needs every tool
* Assign tools based on role: e.g., research gets web search; designer gets image APIs

### Observability

* Log agent decisions, tool calls, outputs, and failures
* Store intermediate artifacts for debugging and auditing

### Error Handling

* Define fallbacks when tools fail
* Handle conflicting agent outputs (prefer primary sources; request re-checks)

---

## Rule of Thumb

Use **Multi-Agent Collaboration** when:

* The task is too complex for one agent
* It can be decomposed into sub-tasks with distinct skills/tools
* You benefit from parallelism and/or explicit quality checks
* You want modularity and agent reuse across projects

---

## Key Takeaways

* Multi-agent systems are a **team design pattern** for complex workflows.
* The biggest decision is the **communication pattern** (linear vs supervisor vs network).
* Specialization + tools per agent improves quality and reduces confusion.
* Critic/reviewer loops often increase correctness and reliability.
* Start simple (linear or supervisor), then evolve to more complex patterns as needed.

---

*Multi-Agent Collaboration turns a single LLM into a coordinated “team” of role-specialized agents—making complex, multi-step work more modular, scalable, and reliable.*

## Corporate SSL proxy note

If you're behind a corporate SSL-inspecting proxy, run examples with:

```bash
AGENTIC_DISABLE_SSL=1 bash run.sh
```

