# Athena - Startup Pitch Narrative

**Format:** Written narrative for accelerator/incubator application
**Audience:** YC / Techstars / similar programs

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## The Pitch

### One-liner

Athena is an AI-native workflow platform where businesses pull and run pre-built AI agent workflows from a CLI. We start with e2e UI testing that anyone can write in plain English.

---

### The Problem

E2E testing is the most hated part of software development. Companies spend 30-40% of their engineering budget on testing, yet test suites are fragile, slow, and constantly breaking. A single UI change can cascade into hundreds of failing tests that take days to fix.

The tooling hasn't fundamentally changed in a decade. Playwright, Cypress, and Selenium all require engineers to write and maintain brittle selectors and scripts. The result: most companies either undertest (shipping bugs) or overinvest in test infrastructure (burning engineering time on maintenance instead of features).

Non-technical team members - product managers, QA analysts, business stakeholders - who understand the product best are locked out of testing entirely because the tools require programming skills.

### The Insight

Two things changed simultaneously:

1. **AI agents can now reliably operate software.** Claude, GPT, and other models can navigate UIs, fill forms, verify states, and reason about what they see - not through fragile CSS selectors, but through understanding the actual interface like a human would.

2. **Agent infrastructure is maturing.** Claude Code's hook system, MCP protocol, and headless execution mode provide a production-grade foundation for building reliable, repeatable AI agent workflows.

This means we can replace hand-coded test scripts with natural language descriptions that AI agents execute against real browsers. No selectors. No page objects. No maintenance hell.

### What Athena Is

Athena is a workflow platform with two sides:

**For the Athena team (us):** We build business-specific AI agent workflows and package them as plugins. Each plugin is a collection of agents, tools, and skills designed for a specific domain.

**For customers:** They install the Athena CLI, pull the workflows they need, and run them. Think of it as `npm` for AI agent workflows - a single command to install and execute sophisticated automation.

Our architecture:

- **Athena CLI** - A terminal client built on Ink (React for CLIs) that manages workflow execution, intercepts agent events in real-time, and provides visibility into what AI agents are doing
- **Plugins** - Self-contained workflow packages containing agents, MCP tool servers, skills, and hooks
- **Hook System** - Real-time interception of AI agent actions via Unix Domain Sockets, enabling approval gates, audit logging, and deterministic control over non-deterministic AI behavior

### The First Workflow: E2E UI Testing

Our first plugin targets e2e UI testing. Here's what it looks like:

A QA engineer or product manager writes:

```
/test Login flow
- Go to the login page
- Enter valid credentials
- Verify the dashboard loads
- Check that the user's name appears in the header
```

Athena's AI agent:

1. Launches a real browser
2. Navigates to the login page
3. Identifies the email and password fields (no selectors needed)
4. Enters credentials
5. Verifies the dashboard loaded correctly
6. Confirms the user's name is visible

If the UI changes - a button moves, a class name changes, a layout shifts - the agent adapts. No test maintenance required.

**What we've built so far:**

- Working CLI with real-time agent event monitoring
- Plugin system with skill definitions, MCP server integration, and configurable isolation
- Browser automation via Chrome DevTools Protocol and custom MCP servers
- Hook-based control system with auto-approve/deny rules and 250ms passthrough timeout
- Session management for multi-step test workflows

### Why Us

We're building on Claude Code's agent infrastructure rather than building agents from scratch. This gives us:

- **Reliability** - Claude Code's tool execution, error recovery, and context management are battle-tested
- **Speed** - We ship workflow plugins, not AI infrastructure
- **Leverage** - As Claude gets smarter, every Athena workflow gets better automatically

### The Market

The software testing market is $52B and growing 14% annually. E2E testing is the fastest-growing segment because companies are shipping faster and need more test coverage.

But we're not just a testing company. Athena is a **workflow platform**. After testing, we expand into:

- **Compliance auditing** - AI agents that verify regulatory requirements across applications
- **Data migration validation** - Agents that compare source and target systems
- **Onboarding automation** - Agents that verify user flows work end-to-end
- **Competitive monitoring** - Agents that track competitor product changes

Each of these is a new plugin on the same platform.

### Business Model

SaaS subscription with tiered pricing:

- **Starter** - Limited workflow runs per month, single plugin
- **Team** - Unlimited runs, multiple plugins, team collaboration
- **Enterprise** - Custom workflows, dedicated support, on-premise deployment

### The Ask

We're looking for [accelerator name] to help us:

1. Find design partners for the e2e testing plugin
2. Validate pricing with real customers
3. Build the team (hiring a second engineer and a go-to-market lead)
4. Refine the platform for the plugin marketplace expansion

---

## Key Talking Points

When presenting, emphasize these:

1. **Demo-driven** - Show the CLI running a natural language test against a real app. The visual impact of "describe a test in English, watch AI execute it in a browser" is powerful.

2. **Platform, not point solution** - Testing is the wedge. The plugin architecture means every new domain is incremental, not a rebuild.

3. **Claude Code foundation** - You're not building AI from scratch. You're building the workflow layer on top of the most capable coding agent. This is a moat: deep integration with Claude's hook system and MCP protocol isn't easily replicated.

4. **The maintenance problem** - Traditional e2e tests break when UIs change. AI agents adapt. This single insight eliminates the #1 pain point in e2e testing.

5. **Three customer personas in one** - QA engineers get faster test creation, developers get less maintenance burden, product managers get direct access to testing. One product serves all three.

---

## Risks to Address Proactively

Accelerators will ask about these. Have answers ready:

| Risk                                                                  | Your Response                                                                                                                                                                           |
| --------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **AI reliability** - Can agents run tests consistently?               | Hook system gives deterministic control. Auto-passthrough timeout ensures agents don't hang. Rules engine can auto-approve/deny specific actions.                                       |
| **Claude dependency** - What if Anthropic changes their platform?     | Plugin architecture is model-agnostic at the workflow layer. MCP protocol is an open standard. Could support other agents in the future.                                                |
| **Cost of AI inference** - Is it economical to run AI for every test? | Cost per test run drops with each model generation. For high-value e2e tests (not unit tests), the cost of AI inference is far less than the cost of an engineer maintaining selectors. |
| **Competitive landscape** - What about other AI testing startups?     | Most are thin wrappers around screenshots + assertions. Athena's hook system provides real-time agent control that others lack. And we're a platform, not just a testing tool.          |
