Agentic Architecture and Inter Agent Communication

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Four core layers of agentic systems

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Modular components of an agent runtime

• Controller / Planner: decomposes high-level goals into actionable tasks and routes work to agents or tools.
• Tool Executor: manages external API integrations, sandboxed function calls, and runtime tools.
• Memory System: stores and retrieves context and long-term state; often uses vector stores for embeddings and semantic search.
• Interface Layer: exposes agent capabilities to users or other systems via APIs, WebSockets, or SDKs.
• Agent Runtime Orchestrator: coordinates planning, tool invocations, retries, and response synthesis.

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Inter-agent communication methods

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Communication patterns

• Request–Response: Agent A asks Agent B to perform work and waits for a result.
• Publish–Subscribe: Agents publish events or tasks; multiple subscribers react or process work.
• Supervisor–Worker: A coordinating agent delegates tasks to worker agents and aggregates results.

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Conversable and collaborative agent capabilities

• Agent customization: personalize agents with domain-specific tools, personas, plugins, or scripts.
• Multi-agent conversations: agents exchange messages for joint reasoning, handoffs, or validation.
• Flexible conversation patterns: support joint chat (a shared channel where all agents contribute) and hierarchical chat (a lead agent delegates to sub-agents).

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Practical multi-agent workflows

• Role-based teams: specialized agents (researcher, writer, fact-checker) collaborate to complete complex tasks.
• Escalation flows: when an agent cannot proceed, it calls a helper or supervisor agent.
• Negotiation: agents propose and score options, exchanging proposals until consensus.
• Pipeline chaining: one agent handles preprocessing and passes results to downstream agents (summarizer → verifier → publisher).

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FastAPI for exposing agent capabilities

• Asynchronous endpoints to integrate with async LLM calls and background tasks.
• Automatic OpenAPI / Swagger documentation generation.
• Strong input validation with Pydantic.
• Easy dependency injection for authentication, policy checks, and observability.

1. Client (user, web app, or another agent) sends a request to the FastAPI service.
2. FastAPI validates the request body with Pydantic models.
3. The application queries an external policy engine (HTTP or SDK) with inputs such as principal, action, and resource.
4. The policy engine returns allow/deny decisions; the application enforces the decision.
5. The decision and relevant metadata are logged for auditing.

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Example FastAPI endpoint (agent interface)

from fastapi import FastAPI, Depends, HTTPException
from pydantic import BaseModel
from typing import Dict

app = FastAPI()

class AgentRequest(BaseModel):
    user_id: str
    prompt: str
    metadata: Dict[str, str] = {}

async def check_policy(user_id: str, action: str) -> bool:
    # Replace with a real policy engine call or SDK
    # e.g., policy_client.evaluate({"principal": user_id, "action": action})
    return True

@app.post("/agent/run")
async def run_agent(req: AgentRequest):
    allowed = await check_policy(req.user_id, "run_agent")
    if not allowed:
        raise HTTPException(status_code=403, detail="policy denied")
    # Invoke planner, memory lookup, and tool executor here (async)
    result = {"status": "ok", "output": "agent result goes here"}
    return result

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How FastAPI fits into agent architectures

• Client sends requests: frontends or other agents call REST endpoints.
• FastAPI endpoints receive validated payloads via Pydantic schemas.
• Agent logic executes: planners, memory lookups, tool executors, and LLM calls run—often asynchronously or via background tasks.
• Response returned: structured JSON responses; auto-generated docs help developer onboarding.

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Deployment and scaling strategies

• Horizontal scaling: multiple worker instances behind a load balancer for stateless agents.
• Service separation: isolate planner, memory, and tool executors into separate services for targeted scaling.
• Async task queues: use Celery, RQ, or cloud-native job queues for long-running or retryable tasks.
• Observability: structured logging, distributed tracing, and metrics for debugging and performance tuning.
• Security and policy enforcement: centralize access control policies, use mTLS, API gateways, and rate limiting.

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Quick reference: communication selection guide

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Links and references

• FastAPI docs and tutorials
• Pydantic documentation
• Redis Streams
• RabbitMQ
• Kafka event streaming course
• Kubernetes basics
• Docker primer