Autonomous Agent Architecture

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1. Overview

Modern Autonomous Agent systems are no longer monolithic prompt loops. They are distributed cognitive pipelines built on four pillars:

Pillar	Responsibility	Primary Components
Reasoning	Decompose intent, evaluate options	LLM, Reasoning, Prompt Engineering
Memory	Persist & retrieve context	Vector Database, Long-term Memory, Episodic Memory
Action	Interact with the world	Tool Use, MCP, Claude Code
Control	Plan, schedule, terminate	Planning, Agent Loop

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2. System Diagram

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3. Execution Lifecycle

The runtime follows a deterministic Agent Loop:

Phase	Bounded by	Failure mode
Retrieve	Context Window	stale embeddings
Plan	token budget	over-decomposition
Act	tool latency	infinite loops
Persist	storage I/O	memory bloat

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4. Memory Hierarchy

Each tier has different access latency and retention:

• Working Memory — microseconds, lost on turn boundary
• Short-term Memory — milliseconds, lost on session end
• Episodic Memory — tens of ms, retained for replay & debugging
• Long-term Memory — ~100ms, indexed in Vector Database (Pinecone)

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5. Tool Layer

Tools are exposed through the MCP protocol. A canonical handler signature:

@mcp.tool(name="search_docs")
              def search_docs(query: str, top_k: int = 5) -> list[Document]:
                  """Semantic search over the engineering Knowledge Base."""
                  embedding = embed(query)              # see Embedding.md
                  return vector_store.query(embedding, k=top_k)

Common tool categories:

Category	Examples	Risk profile
Read-only	search, fetch, list	low
Stateful	write, mutate, delete	high — gate behind Critic
External	API calls, payments	medium — rate limit

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6. Quality Metrics

We track four signals end-to-end:

Metric	Target	Owner
Plan depth	≤ 5 levels	Planner
Tool calls / task	≤ 12	Worker
Memory hit rate	≥ 0.6	Retrieval
Critic acceptance	≥ 0.85	Critic

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7. Reference Implementations

• Claude Code — production-grade coding agent (Anthropic)
• Research Assistant — internal RAG-first research tool
• Code Agent Project — multi-worker code synthesis pilot
• Chatbot Project — conversational frontend, single-loop variant

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8. Open Questions

1. How do we bound Episodic Memory growth without losing rare-but-critical events?
2. Should the Critic LLM share weights with the Planner , or stay distinct?
3. Can Fine-tuning on past Agent Loop traces close the Reasoning gap on long-horizon tasks?
4. What's the right granularity for MCP tools — atomic ops vs composite workflows?

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9. Related Notes

LLM· Transformer· Attention· Tokenization· Embedding· RAG· Prompt Engineering· Fine-tuning· Reasoning· Context Window· Vector Database· LangChain· Ollama· Knowledge Base· ReAct Agent

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