Multi-Agent Systems Development

A multi-agent system deploys multiple specialised AI agents that coordinate on complex workflows - each agent handling a defined function with an orchestration layer managing task routing, context sharing, state management, and error handling across all of them. The analogy is a team of specialists coordinated by a project manager, each expert in one domain, producing a result none could produce alone. The engineering challenge is the coordination, not the individual agents.

Multiple agents add value when: the workflow spans multiple domains requiring different expertise (a legal agent and a financial agent analysing a contract), the volume requires parallel processing, the task requires independent verification by a second agent, the complexity exceeds a single agent's context window, or different steps need different models or tools. If a single agent with better prompting, RAG, or tool calling would solve the problem, adding agents adds coordination overhead without capability. We assess this before building.

Orchestration failures, not agent failures. The five most common: context lost at handoffs (Agent B doesn't receive Agent A's full context), state management with no source of truth (agents operating on conflicting data), observability gaps (debugging five correlated log streams manually), governance that doesn't scale (per-agent policies that compound unexpectedly), and cost opacity (orchestration overhead multiplying in ways budgets didn't capture). The 38% that succeed invest in the orchestration layer before deploying agents.

MCP (Model Context Protocol) standardises how agents connect to tools and data sources - databases, APIs, file systems, enterprise systems. A2A (Agent-to-Agent Protocol) standardises how agents communicate with each other - task delegation, result sharing, and capability discovery. Both are governed by the Linux Foundation with 146 member organisations including Anthropic, Google, OpenAI, Microsoft, and AWS. MCP has crossed 97 million downloads. Building on these standards prevents the custom point-to-point integration problem that makes multi-agent systems unmanageable as they scale.

Five primary patterns: Supervisor (one orchestrator manages all agents), Hierarchical (multi-level coordination), Collaborative (peer-to-peer negotiation), Pipeline (sequential processing), and Parallel Fan-Out (simultaneous execution with result aggregation). Plus a Verification pattern where one agent produces and another verifies. The right pattern depends on your workflow's structure - sequential steps suit pipelines, independent analyses suit fan-out, and complex multi-domain workflows suit hierarchical coordination. We select the pattern based on your workflow, not our preference.

Shared state management with a single source of truth. Every agent reads from and writes to a shared context store, so no agent operates on stale or conflicting information. For tasks where genuine disagreement is possible (analysis, research), the orchestrator is designed to surface and resolve contradictions explicitly rather than silently choosing one agent's output.

More than a single agent - the orchestration layer, observability infrastructure, protocol implementation, and governance architecture add meaningful scope. Key cost drivers: number of agents, orchestration pattern complexity, protocol infrastructure (MCP servers, A2A implementation), observability depth, and governance requirements. The most expensive component is often the orchestration design and testing - ensuring handoffs, state management, and failure recovery work correctly across all agents under real conditions.

End-to-end tracing using OpenTelemetry instrumented across all agents. Every decision, tool call, handoff, and output is traced as one workflow, so you can follow a request from initiation through every agent to the final output. Without this, debugging requires correlating independent log files from each agent manually - which is how problems go undetected for weeks. We treat observability as infrastructure, not a feature.

Yes - and this is usually the right approach. Deploy one agent on your highest-value workflow. Prove the ROI. Then add a second agent when you identify a workflow that genuinely benefits from specialisation and coordination. The orchestration architecture should be designed from the start to support additional agents, but you don't need to deploy them all at once. Incremental deployment with proven value at each step is how the 38% succeed.

Our Customer Support, Sales, HR, Ecommerce, Healthcare, and Voice AI agent pages each cover single-purpose agents for specific domains. This page covers the system that coordinates multiple of them - or coordinates multiple agents within a single domain workflow. If you need one agent, start with the vertical page. If you need multiple agents working together, start here. Many enterprise deployments begin with one vertical agent and evolve into a multi-agent system as the use cases expand.