Many Agents. One Mission.
A single AI model is a tool. A coordinated swarm of agents is a system. I believe the most powerful AI applications won't be built around one model doing everything — they'll be built around many specialized agents working together, each doing what it does best, orchestrated toward a shared goal.
What Is an Agent Swarm?
"One model trying to do everything is a generalist stretched thin. A swarm of specialists is a system built to win."
An agent swarm is a system of multiple AI agents that collaborate to complete tasks too complex, too broad, or too parallelizable for a single agent to handle well. Each agent has a defined role, a set of tools, access to memory, and the ability to communicate with other agents in the swarm.
Think of it like a well-run engineering team. You don't have one person write the code, review it, test it, document it, and deploy it. You have specialists. Agent swarms apply the same principle to AI — decompose the problem, assign it to the right agent, coordinate the results.
The swarm isn't just about parallelism. It's about specialization, redundancy, and emergent capability. A swarm can tackle problems that no single agent could solve reliably on its own.
How I Think About Swarm Architecture
Every swarm I build starts with the same design questions: who orchestrates, who executes, how do agents communicate, and what happens when something fails?
Orchestrator
The top-level agent that receives the goal, decomposes it into subtasks, assigns work to specialist agents, and synthesizes the final result. The orchestrator never does the work itself — it directs.
Specialist Agents
Purpose-built agents with specific tools, prompts, and context windows tuned for their role. A researcher agent, a coder agent, a critic agent, a summarizer — each optimized for one job.
Shared Memory
A shared context store — vector database, key-value store, or structured memory — that lets agents pass information, avoid redundant work, and build on each other's outputs.
Tool Layer
The external capabilities agents can invoke: web search, code execution, database queries, API calls, file operations, MCP servers. Tools are what give agents real-world reach.
Evaluation & Feedback
A critic or evaluator agent that reviews outputs, scores quality, and routes work back for revision when it doesn't meet the bar. Closes the loop and drives quality.
Orchestration Patterns I Use
Different problems call for different coordination patterns. These are the ones I reach for most.
Sequential Pipeline
Agents execute in a defined order, each passing its output to the next. Simple, predictable, easy to debug. Best for linear workflows where each step depends on the previous.
Parallel Fan-Out
The orchestrator splits a task into independent subtasks and dispatches them to multiple agents simultaneously. Results are collected and merged. Dramatically faster for parallelizable work.
Hierarchical Delegation
A top-level orchestrator delegates to sub-orchestrators, each managing their own team of specialists. Scales to complex, multi-domain problems without overwhelming any single agent.
Critic-Revise Loop
A generator agent produces output, a critic agent evaluates it against defined criteria, and the generator revises until the critic approves. Drives quality without human intervention.
What I'm Building
Active experiments and systems in the lab right now.
Enterprise Ops Swarm
A multi-agent system for infrastructure operations. An orchestrator receives incident alerts, dispatches a diagnostic agent to gather system state, a runbook agent to identify remediation steps, and an execution agent to apply fixes — with a human-approval gate before any destructive action.
Claude · MCP Servers · TypeScript · PostgreSQL
Research & Synthesis Swarm
A parallel research system where a planner agent decomposes a research question, dispatches multiple researcher agents to gather information from different sources simultaneously, and a synthesizer agent produces a structured report with citations.
GPT-4o · LangChain · pgvector · Python
Code Review Swarm
A swarm that reviews pull requests using specialized agents for security analysis, performance review, test coverage assessment, and documentation quality — each producing structured findings that a final agent synthesizes into a prioritized review.
Claude · Gemini · TypeScript · GitHub API
Challenges & Lessons Learned
Building swarms in production is harder than building them in demos. Here's what I've learned the hard way.
Context Window Management
Agents accumulate context fast. Without deliberate memory management — summarization, selective retrieval, context pruning — swarms hit token limits and degrade. Every agent needs a memory strategy, not just a prompt.
Failure Propagation
One agent failing silently can corrupt the entire swarm's output. I now treat every agent call as potentially failing and build explicit error handling, retry logic, and fallback paths into every orchestration layer.
Observability Is Non-Negotiable
You cannot debug a swarm you cannot observe. Structured logging of every agent call, tool invocation, and inter-agent message is the difference between a system you can improve and one you can only restart.
The Right Model for the Right Job
Not every agent needs GPT-4o or Claude Opus. Using a fast, cheap model for routing and classification and reserving frontier models for reasoning-heavy tasks cuts cost and latency dramatically without sacrificing quality.
Human-in-the-Loop Is a Feature
The best swarms I've built include deliberate human checkpoints — not as a fallback for when things go wrong, but as a designed part of the workflow. Autonomy is earned incrementally as trust is established.
Tools & Tech Stack
The frameworks, models, and infrastructure I use to build agent swarms.
Orchestration Frameworks
Models
Memory & Storage
Infrastructure
Explore More of the Lab
Agent swarms don't work in isolation — they depend on MCP servers for tools, RAG systems for knowledge, and solid architecture to hold it all together.