Building a Multi-Agent AI System

Multi-agent systems are what you reach for when a single LLM prompt and a long context window stop being enough. You start needing specialization, parallelism, tool orchestration, and long-running workflows that behave more like software systems than chatbots.

I have hit this wall multiple times building production RAG systems and AI agents at Ailog. Most of the hard problems of multi-agent design are familiar from distributed systems and classic software architecture.

This post is about going from "I have one agent with tools" to "I have a coordinated team of agents that actually gets work done".

When do you actually need multiple agents?

Before wiring graphs and message buses, it is worth asking whether multiple agents are justified. Often, better prompt engineering or a solid RAG setup is enough. The following are strong signals that you might benefit from multi-agent design:

1. Distinct skill domains Example: a system that must combine legal reasoning, code generation, and product requirement synthesis. A single system prompt tends to dilute these skills.

2. Different tools, different security boundaries Some tasks need access to private customer data, others should only see anonymized aggregates. Tying this to different agents is easier than trying to conditionally constrain one giant agent.

3. Workflow-like processes For example: retrieve documents using RAG, summarize, then run consistency checks. This pipeline logic already looks like a graph, and multi-agent orchestration generalizes that.

4. Need for parallelism You want multiple subtasks (retrieval, code execution, evaluation) in parallel to reduce latency.

If your use case matches at least two of these, multi-agent is probably worth the complexity.

Core architectural patterns

At a high level, a multi-agent system is just:

• A set of agents (LLM + tools + local state)
• A communication protocol (messages, shared memory, or both)
• A scheduler or orchestrator (who talks to whom, in what order)

You can implement this with raw Python and queues, or with a framework like LangGraph.

Pattern 1: Orchestrator + specialists

The simplest useful pattern is a manager agent that delegates to specialist agents.

• Manager: receives the user request, breaks it into subtasks, routes them
• Specialists: each have a narrow skill, tools, and system prompt

This pattern keeps control and logging centralized, which is valuable for observability and debugging.

Pattern 2: Blackboard (shared memory)

In the blackboard pattern, agents interact indirectly through a shared state store:

• A central state (the "blackboard") contains tasks, intermediate artifacts, and decisions
• Agents read from and write to this shared state

For RAG systems, this shared state often includes:

• Retrieved documents
• Intermediate summaries
• Structured plans
• Evaluation feedback

This maps nicely to graphs in LangGraph, or to explicit Python dicts stored in Redis.

Pattern 3: Peer-to-peer agents

Agents address each other directly, more like microservices. This can be powerful but is harder to reason about and secure. I usually start with orchestrator or blackboard patterns and introduce peer communication only when really needed.

Defining agents in practice

An "agent" in this context is not just an LLM. It is a combination of:

• System prompt defining its role and constraints
• Toolset it can call
• Local memory or state
• Policies (max steps, safety guards, logging)

Here is a minimal but practical agent abstraction in Python using LangChain-like primitives, but you can adapt the idea to any stack.

from typing import List, Dict, Any, Callable
from pydantic import BaseModel

class Tool(BaseModel):
    name: str
    description: str
    func: Callable[[Dict[str, Any]], Dict[str, Any]]

class Agent(BaseModel):
    name: str
    system_prompt: str
    tools: List[Tool]
    llm: Any  # wrap your LLM client

    def _tool_spec(self) -> str:
        return "\n".join(
            f"- {t.name}: {t.description}" for t in self.tools
        )

    def _build_prompt(self, message: str, state: Dict[str, Any]) -> str:
        return f"""You are {self.name}.

{self.system_prompt}

Available tools:
{self._tool_spec()}

Conversation state:
{state}

User request:
{message}

If tools are needed, respond with a JSON object:
{{"tool": <tool_name>, "args": <json args>}}
Otherwise, respond with natural language.
"""

    def __call__(self, message: str, state: Dict[str, Any]) -> Dict[str, Any]:
        prompt = self._build_prompt(message, state)
        raw_output = self.llm(prompt)

        # Simple tool routing logic (pseudo-code)
        try:
            parsed = json.loads(raw_output)
            tool_name = parsed.get("tool")
            if tool_name:
                tool = next(t for t in self.tools if t.name == tool_name)
                result = tool.func(parsed.get("args", {}))
                return {"type": "tool_result", "tool": tool_name, "result": result}
        except Exception:
            pass

        return {"type": "response", "content": raw_output}

This is intentionally minimal, but the core idea is important: tools, state, and message parsing are part of the agent, not scattered across your code.

A concrete multi-agent use case

Let us build something realistic: a requirements assistant for a SaaS product.

The workflow:

1. User describes a feature
2. System analyzes feasibility and architecture
3. System retrieves relevant internal docs and tickets (RAG) to find related work
4. System drafts a detailed spec and acceptance criteria
5. A critic agent evaluates and suggests improvements

This involves at least four agents:

• Planner -- understands the request and decomposes it
• Retriever -- uses semantic search over internal docs
• Spec Writer -- writes the specification
• Critic -- reviews and tightens acceptance criteria

State model and orchestrator

We use a blackboard-like state that all agents can read and write.

from typing import Literal
from pydantic import BaseModel

class SystemState(BaseModel):
    user_request: str
    plan: str | None = None
    retrieved_context: List[str] = []
    spec_draft: str | None = None
    spec_final: str | None = None
    review_comments: str | None = None
    status: Literal[
        "received", "planned", "retrieved", "drafted", "reviewed", "completed"
    ] = "received"

Now a very explicit orchestrator:

class Orchestrator:
    def __init__(self, planner, retriever, spec_writer, critic):
        self.planner = planner
        self.retriever = retriever
        self.spec_writer = spec_writer
        self.critic = critic

    def run(self, user_request: str) -> SystemState:
        state = SystemState(user_request=user_request)

        # 1. Planning
        res = self.planner(
            message="Create a concise numbered plan of steps to handle this request.",
            state=state.dict(),
        )
        state.plan = res["content"]
        state.status = "planned"

        # 2. Retrieval (RAG)
        res = self.retriever(
            message=(
                "Find related features, design docs, and tickets that may impact "
                "this request. Output IDs and short summaries."
            ),
            state=state.dict(),
        )
        state.retrieved_context = parse_retrieval_output(res["content"])
        state.status = "retrieved"

        # 3. Draft spec
        res = self.spec_writer(
            message=(
                "Using the plan and retrieved context, write a detailed spec with "
                "sections: Overview, User Stories, Non-Functional Requirements, "
                "Risks, Open Questions."
            ),
            state=state.dict(),
        )
        state.spec_draft = res["content"]
        state.status = "drafted"

        # 4. Critique
        res = self.critic(
            message=(
                "Review the spec draft. Point out missing edge cases and unclear "
                "requirements. Then propose an improved version."
            ),
            state=state.dict(),
        )
        state.review_comments = res["content"]
        state.spec_final = extract_final_spec(res["content"])
        state.status = "completed"

        return state

This is deliberately synchronous and simple, but it illustrates the key design principle: isolate agent responsibilities and make orchestration explicit.

RAG and multi-agent systems

Multi-agent design pairs well with RAG in several ways.

Dedicated retrieval agents

Instead of every agent doing its own retrieval, create a specialized retrieval agent that:

• Knows which vector database to query and how to pick the right index
• Knows which collections to query
• Applies filtering based on tenant, region, or privacy policies

This centralizes retrieval logic, which is crucial when you have compliance or privacy constraints.

Confidential and public contexts

With strong privacy boundaries, I often split agents into:

• Private context agent -- has access to sensitive user data, applies privacy-preserving techniques at the NLP layer
• Public context agent -- uses public docs, general knowledge, maybe internet search

A manager agent can ask both, then selectively merge their outputs, sometimes after anonymization.

Cross-checking and evaluation agents

Evaluation is where multi-agent design pays off. For example, a fact-checker agent can:

• Take the final answer
• Independently re-run retrieval
• Flag unsupported claims

This is especially powerful in RAG systems where hallucinations can still occur if retrieval is weak or evaluation is missing.

Coordination, control, and failure modes

Once you have several agents, complexity shifts from prompts to coordination. Treat this as an engineering problem.

Step limits and recursion control

Agents that can call other agents can easily fall into infinite loops.

• Enforce a max depth of nested calls
• Track a step count in the shared state
• Make the orchestrator responsible for deciding whether to continue

class SafeOrchestrator(Orchestrator):
    def run(self, user_request: str, max_steps: int = 10) -> SystemState:
        state = SystemState(user_request=user_request)
        steps = 0

        # Pseudocode - suppose each step is a decision by the planner
        while steps < max_steps and state.status != "completed":
            # decide next action based on state
            # call appropriate agent
            steps += 1

        if state.status != "completed":
            # fallback path
            state.spec_final = (
                "System stopped due to step limit. Partial results only. "
                "Please refine your request or contact a human."
            )
        return state

Observability

Logging is essential. At minimum:

• Log each agent call with: input, state summary, output, latency
• Log the orchestration decisions (which agent was chosen and why)

I often store these traces in a simple Postgres table or time series database so I can later analyze:

• Which agents cause the most cost
• Where errors cluster
• How often fallbacks are triggered

Error handling

Have explicit error strategies per agent:

• Retry with backoff for transient failures
• Fall back to a simpler agent or path
• Escalate to a human if impact is high

In practice this means wrapping each agent call and tagging the state with error metadata.

def safe_call(agent, message, state, retries=2):
    for attempt in range(retries + 1):
        try:
            return agent(message=message, state=state)
        except Exception as e:
            if attempt == retries:
                return {
                    "type": "error",
                    "error": str(e),
                    "message": f"Agent {agent.name} failed after retries."
                }

From prototype to production

Once a multi-agent system works on your laptop, getting it to production involves standard web service engineering: containerization, API design, caching, and monitoring.

Serving architecture

Common architecture:

• HTTP / gRPC API gateway: FastAPI is an excellent choice
• Orchestrator service: stateless, reads/writes state to Redis or a DB
• Workers for heavy tools: e.g. code execution, large batch retrieval

Example FastAPI skeleton:

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()

class Request(BaseModel):
    query: str

class Response(BaseModel):
    result: str
    trace_id: str

orchestrator = Orchestrator(...)

@app.post("/feature-spec", response_model=Response)
async def feature_spec(request: Request):
    trace_id = generate_trace_id()
    state = orchestrator.run(request.query)
    store_trace(trace_id, state)  # for debugging & analytics
    return Response(result=state.spec_final, trace_id=trace_id)

Caching and idempotency

Multi-agent flows can be long and expensive. Caching saves cost and reduces latency:

• Cache retrieval results by query + filters
• Cache intermediate artifacts like plan or summarizations
• Make requests idempotent with a client-supplied request ID

Evaluation and continuous improvement

Use multi-agent evaluation loops:

• Automatic evaluators (agents) that score outputs along dimensions like correctness, style, safety
• Compare variants of prompts, tools, or routing policies

The better your base models and embeddings, the more reliable each agent becomes. If you are choosing between embedding providers, the gap between commercial and open-source options has narrowed considerably for multimodal tasks, so benchmark on your own data before committing.

Key Takeaways

• Use multi-agent systems when you have distinct skills, tools, or security boundaries that a single agent struggles to handle.
• Start with simple patterns: an orchestrator plus specialist agents, or a blackboard with explicit shared state.
• Treat each agent as a combination of system prompt, tools, state, and policies, not just an LLM call.
• Combine multi-agent design with solid RAG foundations, including good chunking, retrieval, and privacy constraints.
• Make orchestration explicit in code, with clear state transitions and limits on depth and steps.
• Invest early in observability, logging, and error handling, since debugging interactions is harder than debugging single prompts.
• When moving to production, reuse good practices from web services: FastAPI, Docker, caching, idempotency, and monitoring.
• Agents are useful not only for doing work but also for evaluation, cross-checking, and continuous improvement of the system through autonomous feedback loops.