Artificial Intelligence
Multi-Agent Systems
Coordinated teams of specialised AI agents with defined roles, shared state and supervisory control, for work too broad or varied for a single agent to handle reliably.
Overview
Some problems are too broad for one agent and one prompt. Multi-agent systems divide the work among specialised agents, each with its own instructions, tools and context, coordinated by a supervisor or by a defined protocol.
The pattern adds capability and complexity in equal measure. More agents mean more model calls, more failure modes and harder debugging. We use multi-agent architectures where specialisation measurably improves results, and we engineer the coordination layer, shared memory and observability so the system remains understandable to the people responsible for it.
Business applications
- 01
Customer lifecycle operations
Separate agents handle lead research, qualification, outreach drafting, onboarding and renewal analysis, sharing a common customer record while a supervisor sequences their work and escalates exceptions to account teams.
- 02
Proposal and document assembly
A planning agent decomposes a request for proposal, specialist agents draft technical, commercial and compliance sections from approved sources, and a reviewer agent checks consistency before human sign-off.
- 03
Investigations across many systems
Agents dedicated to logs, transactions, communications and reference data gather evidence in parallel, then a coordinating agent consolidates findings into a structured case file for an investigator to assess.
- 04
Operations and supply planning
Agents representing demand signals, inventory, logistics capacity and supplier constraints propose and test scenarios, giving planners a set of options with stated assumptions rather than a single opaque recommendation.
Engineering approach
- 01
Justify the decomposition
Start from a single-agent baseline and introduce additional agents only where evaluation shows that specialisation, parallelism or separation of permissions improves quality, safety or throughput.
- 02
Define roles and contracts
Specify each agent's responsibility, tools, permissions and the structured messages it exchanges, so handoffs are explicit and every agent can be tested in isolation.
- 03
Choose the coordination pattern
Select supervisor, hierarchical, sequential or event-driven coordination to suit the work, and implement shared state and memory with clear rules about which agents can read and write it.
- 04
Test the system as a whole
Run end-to-end scenarios that exercise failure, disagreement and partial results between agents, measuring cost, latency and outcome quality alongside the correctness of each individual step.
- 05
Instrument and govern
Trace every agent interaction with OpenTelemetry, set budgets and loop limits, expose supervisory dashboards, and define how people intervene when agents stall, conflict or exceed their authority.
Technologies and platforms
Orchestration frameworks
- LangGraph
- Microsoft AutoGen
- CrewAI
- Google Agent Development Kit
- OpenAI Agents SDK
Interoperability
- Model Context Protocol
- Agent2Agent (A2A) protocol
- Apache Kafka
- gRPC
State and memory
- PostgreSQL
- pgvector
- Redis
- Temporal
- Neo4j
Observability
- OpenTelemetry
- Langfuse
- Arise Phoenix
- Grafana
- Datadog
Technology names are trademarks of their respective owners and do not imply partnership or endorsement.
What you receive
- Multi-agent architecture with documented roles and message contracts
- Coordination layer with shared state and memory management
- Individual and end-to-end agent evaluation suites
- Supervisory dashboard with tracing, budgets and intervention controls
- Deployment to your cloud infrastructure with CI/CD
- Runbooks for failure handling and human escalation
Questions leaders ask
When is a multi-agent system the wrong choice?
When a single well-designed agent or a deterministic workflow can do the job. Multi-agent systems cost more to run, are harder to debug and introduce coordination failures. We recommend them when tasks span distinct skills, permissions or data domains, or when parallel work materially changes turnaround, and we validate that choice through evaluation.
How do agents share information safely?
Through structured messages and a shared state store with defined access rules, rather than by passing full conversation histories between agents. Each agent sees the context it needs, sensitive data is scoped by permission, and all exchanges are logged so reviewers can reconstruct how a result was produced.
Can agents built on different platforms work together?
Increasingly, yes. Open protocols such as the Model Context Protocol for tools and Agent2Agent for agent communication make interoperability more practical, though both are still maturing. We design integration boundaries so agents built on different frameworks or model providers can participate, and we test those boundaries explicitly.
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