AI Agent Architecture Blueprint

Supervisor & Worker Swarm (Hierarchical Multi-Agent)

Autonomous multi-agent orchestration architecture where a root Supervisor LLM generates a task DAG, delegates sub-tasks to specialized worker agents (Researcher, Coder, Reviewer), and executes consensus loops.

1. System Requirements

Functional

Decompose ambiguous user goals into a Directed Acyclic Graph (DAG) of parallel and sequential agent sub-tasks.
Dynamically route sub-tasks to specialized worker personas with scoped system instructions and toolkits.
Validate worker outputs against structured acceptance criteria with iterative replanning upon execution failure.

Non-Functional / SLA

Sub-200ms orchestrator routing dispatch overhead (excluding LLM generation time).
Deterministic replayability and full audit trails via distributed OpenTelemetry tracing and structured execution logs.
Strict recursion depth limit (max 5 reflection loops) and budget cap enforcement to prevent runaway token spend.

2. Scale & Resource Estimates

Throughput: 500 concurrent multi-agent swarm workflows (~2,500 sub-agent executions per minute).

Storage & RAM: 35 KB compressed JSON state snapshot per step. 100K workflows/month = 3.5 GB PostgreSQL state store + Redis hot cache.

3. System API Endpoints

// 1. Submit Swarm Orchestration Run
POST /v1/swarms/runs
Content-Type: application/json

{
  "goal": "Audit repo for SQL vulnerabilities, generate patch PR, and verify test suite",
  "orchestrator_model": "claude-3-7-sonnet",
  "max_budget_usd": 4.50,
  "timeout_sec": 300
}

Response (202 Accepted):
{
  "run_id": "swm-88912",
  "status": "orchestrating",
  "dag_nodes": ["repo_indexer", "security_auditor", "patch_generator", "sandbox_tester"]
}

// 2. Stream Real-Time Execution Trajectory
GET /v1/swarms/runs/swm-88912/stream
Response: (Server-Sent Events streaming thought tokens, tool invocations, worker handoffs, and final PR artifacts)

4. Database Schema Design

CREATE TABLE swarm_runs (
    run_id VARCHAR(64) PRIMARY KEY,
    goal TEXT NOT NULL,
    status VARCHAR(32) NOT NULL, -- 'pending', 'running', 'completed', 'failed'
    budget_limit NUMERIC(6,2) NOT NULL,
    spent_usd NUMERIC(6,2) DEFAULT 0.00,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW(),
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

CREATE TABLE swarm_tasks (
    task_id VARCHAR(64) PRIMARY KEY,
    run_id VARCHAR(64) REFERENCES swarm_runs(run_id) ON DELETE CASCADE,
    worker_role VARCHAR(64) NOT NULL, -- 'researcher', 'coder', 'critic'
    dependencies TEXT[], -- Array of predecessor task_ids
    input_payload JSONB NOT NULL,
    output_result JSONB,
    status VARCHAR(32) NOT NULL,
    execution_time_ms INTEGER,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT NOW()
);

CREATE INDEX idx_swarm_tasks_run ON swarm_tasks(run_id);

5. Interactive High-Level Architecture

graph TD User["User Goal / Webhook"] --> Supervisor["Supervisor Orchestrator LLM"] Supervisor --> Planner["DAG Planner Engine"] Planner --> Queue[("Task Queue: Redis / Temporal")] Queue --> WorkerA["Researcher Agent"] Queue --> WorkerB["Coder Agent"] Queue --> WorkerC["Reviewer / Critic Agent"] WorkerA --> ToolsA["MCP Search and Docs"] WorkerB --> ToolsB["Sandbox Runner"] WorkerC --> Verifier["Acceptance Criteria Grader"] WorkerA --> State[("State Store: PostgreSQL")] WorkerB --> State WorkerC -->|Passed| Synth["Final Response Synthesizer"] WorkerC -->|Replan| Supervisor Synth --> Output["Verified Output and Artifacts"]

6. Scalability Deep Dive & Bottlenecks

Hierarchical agent coordination eliminates the chaotic token burn of naive round-robin multi-agent systems. The Supervisor plans a strict DAG with typed input/output contracts (using Pydantic or Zod schemas). If a worker fails verification (e.g., unit tests fail in the Coder agent), the Critic emits a structured failure diagnosis back to the Supervisor, triggering targeted replanning of only that sub-graph node rather than restarting the entire conversation history from scratch.

7. Production Scaling Strategies

Durable Execution Engines: Implement worker loops using Temporal or LangGraph Checkpointers so tasks automatically resume from the last known state after spot instance interruptions or timeouts.

Shared Context Caching: Keep system prompts, repository schemas, and architectural guidelines static across worker calls to unlock 90% prompt caching cost savings on Gemini, Anthropic, and OpenAI.

Concurrency Fan-Out: Independent DAG branches (e.g. parallel security scans across 5 microservices) execute concurrently across serverless worker pools to reduce wall-clock time from minutes to seconds.

8. Runnable Production Starter Code

Scaffold

System Design Architectures