A multi-agent system uses multiple specialized AI agents working together on a task too large, complex, or multi-domain for a single agent to handle reliably. Context management in multi-agent systems is fundamentally different from single-agent context management — each agent has its own context window, and the challenge is ensuring the right knowledge reaches the right agent at the right time.
Why multiple agents
Single agents hit practical limits: context windows fill up on large tasks, a single model can't hold expertise across all domains simultaneously, and long-running single agents accumulate errors that compound through the task. Multi-agent architectures address this by decomposing tasks across agents that can work in parallel, specialize in specific domains, and independently verify each other's work.
A common pattern in software development: an orchestrator agent decomposes a feature into subproblems and delegates them to specialist agents — one for frontend implementation, one for backend API design, one for test generation, one for documentation. Each specialist runs with a focused context window; the orchestrator synthesizes their outputs.
Context sharing patterns
The central challenge in multi-agent systems is ensuring agents that depend on each other's work have access to relevant context. Three main patterns:
- Broadcast — the orchestrator includes shared context (project specs, architectural decisions, interface contracts) in every subagent's initial context. Simple but expensive: the shared context is included redundantly in every agent call.
- On-demand retrieval — agents share a common knowledge store (vector database, document store) and retrieve what they need. More efficient; requires retrieval infrastructure and careful retrieval quality management.
- Message passing — agents communicate through structured messages containing both the task and the relevant context for that specific handoff. Precise and efficient; requires careful message schema design.
In practice, hybrid approaches work best: broadcast for small, high-value shared context (interface contracts, core constraints); retrieval for large reference material; message passing for specific task handoffs.
Shared knowledge stores
A shared knowledge store — a document or database that all agents can read and write — is the multi-agent equivalent of a single-agent's context. Well-designed shared stores have: clear ownership rules (which agent is authoritative for which information), structured schemas that agents can parse reliably, and versioning so agents can tell when information was last updated.
Project planning documents are natural shared knowledge stores for software development multi-agent systems. The architecture spec is authoritative for technical decisions; the PRD is authoritative for requirements; user stories are authoritative for scope. Each agent reads from the relevant documents and writes its outputs to designated sections — a frontend agent updates the UI implementation notes; a backend agent updates the API implementation notes.
Coordination patterns
Multi-agent coordination follows a few core patterns:
- Hierarchical — a single orchestrator directs workers. Simple control flow; the orchestrator is a bottleneck and single point of failure.
- Pipeline — agents hand off sequentially, each consuming the previous agent's output. Predictable; failures cascade in one direction.
- Peer-to-peer — agents communicate directly without a central coordinator. Flexible; requires careful design to avoid coordination chaos.
- Voting / ensemble — multiple agents independently produce outputs that are merged or voted on. High quality; high cost.
Context conflicts and resolution
When multiple agents write to a shared knowledge store, conflicts can occur: two agents reach different conclusions about the same fact, or one agent's output is based on stale context from before another agent's update. Conflict resolution strategies: last-write-wins (simple, may lose information); explicit versioning with merge logic; and a dedicated reconciler agent that reviews conflicts and produces authoritative resolutions.
The most common source of context conflicts is agents working from stale snapshots of a shared document. Timestamping every write and including timestamps in every read allows agents to detect staleness and request refreshed context before acting on outdated information.
Practical implementation
Start with the simplest multi-agent architecture that addresses your problem: usually hierarchical, with an orchestrator and 2–3 specialized workers. Define the shared knowledge store schema before writing any agent code. Build message-passing interfaces with explicit schemas. Test each agent in isolation before testing coordination. Add complexity — more agents, richer coordination — only when the simpler architecture proves insufficient.
See AI Agent Architecture Design for single-agent foundations, or AI Workflow Orchestration Tools for tooling that handles multi-agent coordination.