Beyond Chat: Building a Team of AI Agents

How Most People Use LLMs

Most people interact with AI the same way: open a chat window, type a question, get a response, copy-paste the useful parts, and move on. Maybe refine the prompt, iterate a few times, and eventually assemble something workable.

The workflow looks like this:

1. Think of what you need
2. Write a prompt
3. Read the output
4. Copy the useful parts
5. Manually integrate it into your project
6. Repeat

It works. For small tasks — writing a function, debugging an error, drafting an email — the chat-based approach is genuinely effective. Many people have experienced the thrill of getting a working solution in seconds.

But this approach breaks down completely for complex projects. And understanding why it breaks down reveals something important about where AI is headed.

Why Single-LLM Chat Fails at Scale

Consider this project: you want to build a full-stack web application for managing chess tournaments. It needs:

• A database schema for players, tournaments, rounds, pairings, and results
• A backend API with authentication, pairing algorithms (Swiss system, round-robin), and ELO rating calculations
• A frontend with real-time bracket displays, player profiles, and admin dashboards
• Integration with external chess APIs for importing game data
• Automated testing for the pairing algorithm (which has subtle edge cases around byes, late entries, and tiebreakers)
• CI/CD pipeline and deployment configuration

Try building this by chatting with a single LLM. Here’s what happens:

Context window limits. Even with 100–200K token context windows, your project quickly exceeds capacity. The model loses track of decisions made earlier, generates code that contradicts other code it wrote ten messages ago, and forgets the database schema when working on the frontend. You start every conversation re-explaining things the model already “knew.”

No persistent state. Each conversation starts fresh or relies on increasingly stale context. There’s no durable memory of what was built yesterday. You become the integration layer — manually ensuring consistency across files, components, and architectural layers.

Single-threaded thinking. The LLM works on one thing at a time. It can’t simultaneously reason about the database schema while testing the pairing algorithm while designing the API. You become the scheduler, the context-switcher, the person who holds the full picture while feeding fragments back and forth.

No verification loop. In a basic chat interface, the LLM generates code but can’t run it, test it, or verify it works in context. You’re the tester. For a complex pairing algorithm with dozens of edge cases, this manual verification becomes the bottleneck, not the code generation.

No specialization. The same model is acting as database architect, backend engineer, frontend developer, and DevOps specialist — all in one conversation. It has no way to adopt different “modes of thinking” for different tasks, and no way to bring focused expertise to a specialized problem.

The result: you spend more time managing the LLM than building the product. For anything beyond moderate complexity, the efficiency gain evaporates.

The Agent Team Model

What if, instead of one LLM answering your questions, you had a team of specialized agents — each with their own tools, context, and responsibilities — coordinated by an orchestrator that understands the overall architecture?

This is the agent team model. It mirrors how real engineering teams work, and for the same reasons.

The diagram above shows the full architecture. The orchestrator delegates tasks downward. Every agent reads from and writes to a shared context layer. The QA engineer feeds review results back up to the orchestrator, closing the quality loop. Each agent has its own specialized tools.

Let’s break down each component.

The Orchestrator

The orchestrator is the tech lead. It receives the high-level goal, breaks it down into subtasks, assigns them to specialist agents, and integrates the results. It maintains the architectural vision and resolves conflicts.

The orchestrator doesn’t write much code itself. Instead, it:

• Decomposes the project into workstreams with clear dependencies
• Assigns tasks to specialist agents
• Reviews outputs for cross-cutting consistency
• Resolves conflicts when two agents’ outputs are incompatible
• Maintains a shared context document that all agents reference

Specialist Agents

Each specialist agent is configured with:

• A system prompt defining its role and expertise (e.g., “You are a database architect specializing in PostgreSQL schema design”)
• Tools it can use: file read/write, database queries, API calls, test runners, linters
• A focused context window containing only the information relevant to its current task
• Output contracts specifying what it must produce (e.g., “a SQL migration file and a typed data access layer”)

For the chess tournament app, your team might include:

Each agent is good at one thing because it has focused context, specialized tools, and a clear mandate. This is the same reason software teams have roles.

The Shared Context Layer

Agents need a way to share information without passing entire codebases around. The shared context layer is a structured set of documents that serves as the project’s source of truth.

It contains:

• The database schema — so the frontend agent knows the data shapes without reading the migration files
• API contracts — so frontend and backend stay synchronized without direct communication
• Architecture decisions — so no agent contradicts a settled design choice
• A task board — so agents know what’s done, what’s in progress, and what’s blocked

Each agent reads from this shared context before starting work and writes updates when it produces output that others depend on. The schema changes? The shared context is updated, and downstream agents are notified.

The Review Loop

After each agent completes a task, its output goes through a review loop:

1. The specialist agent produces its output (code, tests, config files)
2. The QA agent reviews for correctness, edge cases, and test coverage
3. The orchestrator checks consistency with the overall architecture
4. If issues are found, the task goes back to the specialist with specific feedback
5. Once approved, the output is merged into the shared codebase

This mimics code review in real teams — and for the same reason. The review loop catches errors that any single agent would miss. A backend agent might generate a perfectly working API endpoint that uses column names that don’t match the actual schema. Without a review loop, that bug propagates. With one, it’s caught immediately.

Infrastructure for Agent Teams

Building a functional agent team requires some infrastructure. None of it is conceptually complex, but it’s what separates “chatting with an AI” from “orchestrating AI workers.”

Task Queue and Dependency Graph

A system that tracks which tasks are pending, in progress, completed, or blocked. Each task has:

• An owner (which agent is responsible)
• Dependencies (which tasks must complete first)
• Input artifacts (what the agent needs to start)
• Output artifacts (what the agent must produce)

The orchestrator reads this state to make scheduling decisions. When the Database Architect completes the schema, the Backend Engineer and Frontend Engineer can start in parallel — their tasks were blocked on the schema, and now they’re unblocked.

Context Curation

Each agent gets a tailored context window. Instead of stuffing the entire project into one context, you curate what each agent sees:

• The Database Architect sees the requirements document and the existing schema
• The Backend Engineer sees the schema, API contracts, and authentication requirements
• The Frontend Engineer sees the API contracts, design mockups, and component library

This focused context produces dramatically better output than a single model trying to hold everything at once. It’s the difference between asking a generalist “build me a database and an API and a frontend” versus telling a specialist “here’s the schema, build endpoints for these five operations.”

Tool Integration

Agents need to do things, not just generate text. A backend agent that can run its own tests catches bugs immediately instead of producing untested code. A DevOps agent that can execute Docker builds catches configuration errors on the spot. A QA agent that can run the full test suite and report results provides actual verification, not just “this looks right.”

Tools transform agents from “text generators that happen to write code” into “workers that build, test, and verify software.”

Communication Protocol

Agents need to send targeted messages to each other when their work has cross-cutting implications:

• “The database schema changed — column rating is now elo_rating.”
• “The API endpoint /api/pairings now returns a different response shape.”
• “I discovered that the Swiss pairing algorithm needs a bye_player_id field — please add it to the rounds table.”

Without this communication layer, agents work with stale assumptions and produce outputs that don’t integrate. With it, the team adapts fluidly as the project evolves.

What This Looks Like in Practice

Here’s a simplified execution trace for building the chess tournament app:

Orchestrator: Analyzing project requirements...
Orchestrator: Creating task breakdown:
  [DB-1]  Design database schema           → Database Architect
  [BE-1]  Implement authentication          → Backend Engineer
  [PA-1]  Implement Swiss pairing system    → Pairing Specialist
  [FE-1]  Set up frontend scaffolding       → Frontend Engineer
  [QA-1]  Write pairing algorithm tests     → QA Engineer

  Dependencies: BE-1, PA-1, FE-1 depend on DB-1

Database Architect: Working on DB-1...
  → Reads: requirements document
  → Produces: schema.sql, migrations/, models.py
  → Updates shared context: schema documentation
  → Status: COMPLETE

--- DB-1 complete. Unblocking BE-1, PA-1, FE-1. ---

Backend Engineer: Working on BE-1...         [parallel]
  → Reads: schema from shared context
  → Produces: auth.py, middleware.py, tests/test_auth.py
  → Runs tests: 12/12 passing
  → Status: COMPLETE

Pairing Specialist: Working on PA-1...       [parallel]
  → Reads: player/tournament schema
  → Produces: swiss_pairing.py, round_robin.py
  → Runs tests: 23/23 passing
  → Status: COMPLETE

Frontend Engineer: Working on FE-1...        [parallel]
  → Reads: API contracts from shared context
  → Produces: components/, pages/, api-client.ts
  → Status: COMPLETE

QA Engineer: Reviewing PA-1 output...
  → Finds edge case: odd number of players with late entry
  → Sends back to Pairing Specialist with failing test

Pairing Specialist: Fixing edge case...
  → Updates: swiss_pairing.py (handles bye assignment for late entries)
  → Runs tests: 24/24 passing (including new edge case)
  → Status: COMPLETE

Orchestrator: All phase-1 tasks complete.
Orchestrator: Creating phase-2 tasks:
  [BE-2]  Implement pairing API endpoints   → Backend Engineer
  [FE-2]  Build tournament bracket UI       → Frontend Engineer
  [DO-1]  Set up CI/CD pipeline             → DevOps Agent
  ...

Three agents worked in parallel as soon as their dependency (the schema) was resolved. The QA agent caught an edge case that the specialist missed. The orchestrator managed the phasing without any human intervention.

No single LLM session could manage this coordination. And no human should have to serve as the manual bridge between all these moving parts.

The Shift in Skills

The transition from “chatting with an AI” to “orchestrating a team of AI agents” mirrors the transition from “writing all the code yourself” to “leading an engineering team.”

The skills change:

• Instead of writing better prompts, you design better systems
• Instead of wrestling with context limits, you architect information flow
• Instead of manually verifying output, you build automated review pipelines
• Instead of doing one thing at a time, you parallelize workstreams

This is where the real leverage is. A well-designed agent team can accomplish in hours what a single LLM chat session can’t accomplish at all — not because the individual models are smarter, but because the system is smarter.

We’re Early

We’re very early in this transition. The tooling is still being built, the patterns are still emerging, and most people — even most developers — haven’t yet made the leap from “AI as a chat tool” to “AI as a team of workers.”

But the trajectory is clear. The same way that software engineering evolved from solo programmers to coordinated teams with specialized roles, version control, CI/CD, and code review — AI usage will evolve from single-model chat to coordinated multi-agent systems with task management, shared context, specialized tools, and review loops.

The question isn’t whether AI agents will work in teams. It’s whether you’ll be the one designing those teams — or the one being surprised by what they build.