What a multi-agent system actually is.

A multi-agent system is a set of specialised AI agents that coordinate to complete a shared goal faster and more reliably than a single monolithic agent.

Five-agent architecture diagram

A coordinator shares tasks, each agent returns artifacts, and a policy agent validates before handoff.

• Spark — planner agent that converts goals into executable task graphs.
• Pixel — design and UI agent that composes layouts and visual assets.
• Ledger — data and state agent that tracks dependencies, budgets, and artifacts.
• Sentinel — risk and policy agent enforcing validation, auth, and compliance checks.
• Rook — execution agent handling deployment steps, retries, and handoffs.

Typical handoffs:

• Spark → Pixel: layout directives
• Pixel → Ledger: asset and state sync
• Ledger → Sentinel: policy check request
• Sentinel → Rook: approved execution

Communication patterns

Three topologies cover most production systems. Pick based on how much coordination overhead the workflow can absorb.

• Hub-and-spoke coordination. A coordinator assigns tasks to specialist agents and merges outputs into a single execution plan.
• Peer-to-peer negotiation. Agents exchange proposals directly when tasks overlap, then commit a conflict-free version.
• Event-driven broadcast. Agents publish state updates to a shared bus so interested agents react without tight coupling.

Real example: 7 sites built overnight

End-to-end execution window: 22:00 to 04:30.

• 22:00 — Spark decomposed a brief into seven site scopes and parallelised delivery tracks.
• 23:30 — Pixel generated page systems while Ledger synchronised assets, content, and acceptance criteria.
• 01:00 — Sentinel blocked unsafe actions, enforced schema checks, and routed uncertain items for review.
• 04:30 — Rook executed builds, retries, and deploy flows; all seven sites shipped before morning standup.

Result: seven production-ready sites shipped by morning with shared QA and policy controls.

Frequently asked

What's the main advantage?

It distributes work across specialised agents so teams can parallelise complex workflows while keeping quality controls in place.

Do they always need a central coordinator?

Not always, but most production systems use at least a lightweight orchestrator to reduce conflicts and improve observability.

How do you prevent agent conflicts?

Shared state contracts, clear ownership boundaries, and arbitration rules when two agents propose incompatible changes.

When should teams avoid multi-agent design?

If one deterministic pipeline solves the problem, adding multiple agents increases overhead without meaningful payoff.