Custom Multi-Agent Systems

What Is a Multi-Agent System?

A multi-agent system (MAS) is a network of AI agents that collaborate on complex tasks — each agent specialized for a specific function, all coordinated through a shared communication and orchestration layer.

Where a single agent handles a linear task, a multi-agent system handles complexity: parallel research threads, hierarchical decision-making, distributed execution across heterogeneous data sources. The architecture is what scales intelligence.

When Do You Need Multi-Agent vs. Single Agent?

A single agent is sufficient when tasks are sequential and bounded — a defined set of steps, limited tool use, one decision chain.

You need a multi-agent system when:

• Tasks are parallel — multiple independent sub-tasks that can run concurrently
• Tasks require specialization — different steps require fundamentally different capabilities or model configurations
• Tasks are long-horizon — the process spans hours, days, or involves persistent state
• Tasks are adversarial — you need agents to critique each other's work (research + verification patterns)
• Volume requires distribution — the workload must scale across multiple concurrent agent instances

The Architecture Patterns We Use

Supervisor-Worker

The most common pattern. A supervisor agent holds the high-level goal, decomposes it into sub-tasks, assigns them to workers, and aggregates results. Workers are isolated — they can't communicate with each other directly, only with the supervisor.

Best for: Document analysis, research pipelines, structured data extraction at scale.

Blackboard Architecture

Agents communicate through a shared, typed state store (the "blackboard"). Each agent reads current state, contributes its output, writes back. No direct agent-to-agent messaging. The blackboard schema is strictly validated.

Best for: Collaborative document creation, multi-perspective analysis, iterative refinement workflows.

Event-Driven Choreography

Agents subscribe to event channels. When one agent completes work, it emits an event. Other agents subscribed to that event type execute their next action. The workflow emerges from event chains — no central orchestrator.

Best for: High-throughput processing, asynchronous workflows, systems where agents need to scale independently.

Memory Architecture

Every agent in our systems has access to three memory layers:

1. Working memory: The agent's current context window — what it's reasoning about right now
2. Episodic memory: A queryable record of past actions and outcomes — what it has done before
3. Semantic memory: A vector store of domain knowledge — what it knows about your business

Stateless agents that reconstruct context from memory are dramatically more reliable and debuggable than stateful agents that carry context between invocations. We design for statelesness by default.

Observability First

Every agent action in our systems is instrumented with:

• Unique trace ID (linking the full decision chain across agents)
• Input hash and output hash
• Execution time and token consumption
• Tool calls made and their results
• Confidence scores where applicable

You can replay any agent execution, inspect every decision, and identify exactly where a workflow deviated from expected behavior. Observability isn't an afterthought — it's a design requirement.