AI Agents & Multi-Agent Systems

One agent with forty tools and a three-page prompt performs worse than four agents with ten tools each. Attention degrades as context grows, tool selection gets less accurate, and debugging becomes guesswork. Splitting the work into specialists with narrow remits is usually the fix — but it introduces coordination problems that need designing rather than hoping through.

When you actually need more than one agent

Not as often as the hype suggests. A single well-scoped agent handles most tasks, and every additional agent adds latency, cost and failure modes. Multiple agents earn their place when the work spans genuinely different domains, when steps can run in parallel, or when one agent's output needs independent review before it is trusted. We will tell you when one is enough.

Who this is for

Teams whose single agent has grown unwieldy — too many tools, a prompt nobody wants to touch, accuracy sliding as scope creeps. Companies automating workflows that cross departments, where each stage needs different systems and different judgement.

Problems we solve

• Context dilution. One agent carrying every instruction for every scenario and doing all of them worse.
• Lost handoffs. Work passed between agents with the important context dropped on the way.
• Cascading errors. One agent's mistake accepted downstream as fact.
• Runaway cost. Agents calling agents in loops with no global budget.
• No accountability. A wrong output and no way to identify which agent produced it.

What we build

• Supervisor architectures where a coordinator decomposes work and delegates to specialists
• Sequential pipelines with typed, validated handoffs between stages
• Parallel execution with result aggregation, where steps are genuinely independent
• Critic and reviewer agents that check another agent's output before it is acted on
• Shared state and memory, so agents do not repeat each other's work
• Global budgets and depth limits across the whole system, not per agent

How we work

We begin with a single agent and split only when we can show why. Each split is justified by a measurable problem — accuracy on an evaluation set, latency, or an unmanageable tool count. Handoffs use structured schemas rather than free text, because prose handoffs lose information silently. The whole system gets one trace, so a run can be read end to end rather than reconstructed from fragments.

Technologies we use

Claude, GPT and Gemini, often mixed — a cheaper model for routing and classification, a stronger one for reasoning. LangChain and LlamaIndex for orchestration, MCP for shared tool access, Redis for shared state and coordination locks, and n8n where the orchestration benefits from being visible to non-engineers.

Business benefits

• Broader processes automated without accuracy collapsing as scope grows
• Parallel stages cut end-to-end time on multi-part work
• Model spend matched to task difficulty rather than paying premium rates for routing
• Specialists can be improved, tested and replaced independently

Where it pays off

• Research tasks: one agent gathers, another verifies, a third writes
• Support flows where triage, knowledge lookup and resolution need different tools
• Content pipelines with a separate review stage before publication
• Onboarding that spans sales, finance and provisioning systems

Common questions

Is a multi-agent system always better?

No, and treating it as a default is a common and expensive mistake. More agents mean more latency, more tokens and more ways to fail. We use one agent until there is evidence that one is not enough.

How do you stop agents talking in circles?

Depth limits, global step budgets, and a supervisor that owns termination rather than leaving agents to decide among themselves when to stop.

Can different agents use different models?

Yes, and they usually should. Routing and classification run well on small fast models; only the genuinely hard reasoning needs a frontier model. This is often the single largest lever on running cost.

Has your single agent outgrown itself? We will review the traces and tell you whether splitting it will help.