Agent Track

Use this path if you are integrating AE into an AI agent runtime or MCP-capable client.

Prerequisites

• Access to CLI execution environment
• Ability to parse structured command output
• Optional: MCP-compatible client

Step 1: Discover machine-oriented docs

• Read /llms.txt for indexed doc entry points.
• Read /llms-full.txt for consolidated guidance.

Step 2: Validate executable contract

Run:

ae definition
ae verify --input verify.json

Expected result:

• Stable envelope structure.
• Deterministic error codes on failure paths.

Step 3: Execute one agent flow

ae instructions --context project --action use

Then retrieve one registry action:

ae registry get --library-id python_requests --action use --out ./ae_use

Step 3.5: Use knowledge extraction

Initialize hub and extract domain knowledge for context-aware operations:

ae hub init
ae know build --url https://modelcontextprotocol.io/llms-full.txt --name mcp
ae generate --library-id dart_mcp --library-root . --know mcp --engine template --dry-run

The --know flag enriches generation with extracted domain knowledge.

Step 4: MCP integration

Use the MCP adapter docs in MCP integration and keep tool payloads typed.

First-success complete when your agent can discover docs, execute one command, and recover from one simulated error with documented code-based handling.