Multi-Agent Systems
Fleets of governed agents coordinated around real workflows — not a demo that stalls after the meeting.
Multi-agent demos are easy to build and difficult to trust. Agents delegating to agents produces impressive transcripts and, in production, compounding failure: one agent misreads a result, passes it on with confidence, and three steps later the system has taken an action nobody would have approved.
The engineering problem is not making agents talk to each other. It is bounding what the conversation can cause — which is a question about identity, authorisation, and where the human sits, not about the orchestration framework.
This engagement designs and delivers agent fleets that are coordinated, bounded, and observable.
What the engagement includes
Workflow and role decomposition
Which agents exist, what each is responsible for, and where the boundaries sit — modelled on the actual business workflow rather than on the framework's abstractions.
Orchestration patterns
ReAct, plan-execute-reflect, and agent-to-agent delegation applied where each genuinely fits, with LangGraph, LangChain, or MCP-based context integration as the implementation.
Human-in-the-loop gates
Approval points placed where blast radius justifies them, designed so the human sees enough context to make a real decision rather than rubber-stamping.
Per-agent identity and authorisation
Each agent a distinct principal with its own scoped credentials, so a compromised or confused agent is bounded by what it alone may do.
Observability across the fleet
Tracing that follows a task across agent handoffs, so a wrong outcome can be traced to the step that caused it.
Who this is for
- Workflows genuinely requiring specialised agents rather than one well-prompted model
- Teams whose multi-agent prototype is unpredictable at scale
- Organisations that need agent fleets auditable per action
When this is the wrong engagement: Many problems presented as multi-agent are better served by a single agent with good tools. If that is the case, we will say so during discovery — it is cheaper for you and more reliable.
How it runs
- Discovery (30 minutes). A working call to map your stack, constraints, and the highest-value first step. No pitch deck.
- Scoped fixed-price proposal. A written statement of work with deliverables, milestones, and a fixed price — not an open-ended time-and-materials meter.
- Delivery. Senior architects execute against milestones, with governance applied to every AI-agent action from day one.
- Operate or hand off. Monitored operation, or a clean handoff with runbooks so your team can run it without us.
Questions we get asked
Do we actually need multiple agents?
Frequently not, and this is worth resolving before building anything. Multiple agents earn their complexity when subtasks need genuinely different tools, permissions, or models, or when parallelism materially changes latency. When the real driver is that one prompt became unwieldy, the better fix is usually decomposition within a single agent. Discovery answers this explicitly rather than assuming the premise.
Which framework do you build on?
LangGraph and LangChain where they fit, MCP for context and tool integration, and plain code where a framework would add more indirection than value. The orchestration framework is one of the least consequential decisions in a multi-agent system, and one of the easiest to change later. Identity, authorisation, and human-gate placement are the decisions that are expensive to revisit.
How do you stop agents compounding each other's mistakes?
By bounding authority rather than trying to make each agent correct. Every agent has its own scoped credentials, every action is authorised against policy at the point of execution, and irreversible actions sit behind human gates. A confused agent then produces a bad suggestion rather than a bad outcome. Evaluation on the workflow end-to-end catches the compounding cases that per-agent testing misses.
Can this integrate with our existing systems?
Yes, and that is usually where most of the work is. Agents earn their value by acting in real systems, which means integration through MCP servers or APIs, with credentials scoped per agent and per system. The integration surface, not the agent logic, is normally the larger part of the engagement and is scoped explicitly.
Related services
AI Governance
Identity per agent, autonomy tiers, and audit trails your risk and security teams will actually accept.
Enterprise AI Architecture
Reference architectures for agents, RAG, and MLOps that hold up under real load, security review, and audit.
AI Security Consulting
Threat modeling for LLMs, data pipelines, and model endpoints — plus the controls to close the gaps.
Start with a 30-minute discovery call
No pitch deck. We map your constraints and tell you the highest-value first step — including when that step is not an engagement with us.
Book a discovery call