AI Agent Architecture

1 What Is an AI Agent?

2 The Perception-Reasoning-Action Loop

# Simplified agent execution loop
def run_agent(task, max_steps=20):
    memory = AgentMemory()
    context = build_context(task, memory)

    for step in range(max_steps):

        # REASON: LLM picks next action
        response = llm.complete(context)

        if response.is_final_answer:
            return response.output

        # ACT: Execute tool call
        tool_result = execute_tool(
            response.tool_name,
            response.tool_args
        )

        # OBSERVE: Feed result back
        context = update_context(
            context, tool_result
        )
        memory.store(step, tool_result)

    return "Max steps reached"

3 Short-Term Memory

4 Long-Term Memory

Long-term memory is persistent storage outside the context window that the agent can selectively read from and write to. This enables agents to recall facts, preferences, and past experiences across different sessions and even different users.

# Write to long-term memory after a task
async def save_to_memory(agent_id, content, memory_type):
    embedding = await embedder.embed(content)
    await vector_db.upsert({
        "id": uuid(), "vector": embedding,
        "payload": { "agent_id": agent_id, "content": content,
            "memory_type": memory_type, "ts": now() }
    })

# Retrieve relevant memories before a task step
async def retrieve_memories(query, agent_id, top_k=5):
    q_vec = await embedder.embed(query)
    results = await vector_db.search(
        vector=q_vec, filter={"agent_id": agent_id}, limit=top_k
    )
    return [r.payload["content"] for r in results]

5 RAG & Vector Databases

6 Planning & Reasoning Patterns

Planning converts a high-level goal into a sequence of executable actions. Modern agents use several established patterns — often combining them within a single workflow.

7 Tool Integration Layer

# Define a tool for the agent
tools = [{
    "name": "web_search",
    "description": "Search the web for
        real-time information",
    "parameters": {
        "type": "object",
        "properties": {
            "query": {
                "type": "string",
                "description": "Search query"
            },
            "max_results": {
                "type": "integer",
                "default": 5
            }
        },
        "required": ["query"]
    }
}]

# LLM emits this structured call:
# { "name": "web_search",
#   "arguments": {"query": "GDP 2025"}}

8 Multi-Agent Architecture

Complex tasks often exceed the capabilities of a single agent. Multi-agent systems distribute work across specialized agents that collaborate, check each other's work, and operate in parallel — increasing both capability and reliability.

9 Production Considerations

10 FAQ