Multi-Agent Systems
Several agents on one process, when a single agent genuinely is not enough.
What is Multi-Agent systems?
A multi-agent system is several AI agents working on one process, each handling a defined part and passing work to the next through explicit handovers, with a single place to look when something goes wrong.
The obvious appeal is that one agent handles the enquiry, another checks stock, another raises the order, and the process runs end to end without a person.
The part rarely mentioned is that every handover is a place to fail, and failures across agents are much harder to diagnose than failures inside one. Three agents is not three times the complexity, it is considerably more.
Multi-agent designs earn their cost on genuinely long processes that cross several systems and where a single agent has already proved insufficient. That last condition matters most.
Why we usually recommend one agent
Duara designs multi-agent systems from Nairobi, and we say plainly that most businesses asking for one do not need it.
The usual honest answer is a single agent doing one thing properly. We would rather tell you that at the review stage than build an architecture whose main achievement is being difficult to debug. We have talked more clients out of this than into it.
Who this is for
- A single agent has been tried and is genuinely insufficient
- Your process crosses several systems end to end
- Different steps need genuinely different capabilities
- You already run agents successfully and want them joined up
Industries we serve
- Logistics and freight
- Banking operations
- Insurance claims
- Large scale manufacturing
- Telecommunications
- Group retail operations
Problems this solves
These are the narrow conditions where the extra complexity is actually justified.
- A process too long for one agent. The work crosses several systems and stages, and a single agent handling all of it becomes unreliable at the edges.
- Steps needing different capabilities. Reading documents, querying data and drafting replies are different jobs, and one agent doing all three does each less well.
- Agents already running in isolation. You have working agents that hand off to people unnecessarily because nothing connects them.
- No single view when something breaks. A failure somewhere in a chain of steps is difficult to locate and slow to fix.
What we deliver
A designed system of agents with explicit handovers, defined responsibility at each step, one place to observe the whole process, and a human checkpoint wherever the cost of a wrong step is high.
A designed system with each agent responsibility defined
Explicit handovers between steps
One place to observe the whole process
Human checkpoints where the cost of error is high
Failure handling at every handover
An honest assessment of whether you need this at all
Features
- We will tell you not to. Where a single agent would do, we say so. We have talked more clients out of this than into it.
- Handovers made explicit. Every pass between agents is defined and observable, because handovers are where these systems actually fail.
- One place to look. The whole process is observable in one view, since a failure inside a chain is otherwise slow to locate.
- Checkpoints where it matters. A person sits at any step where a wrong action is expensive, rather than at the end where it is too late.
- Built incrementally. Agents are added one at a time, each proven before the next, rather than designed as a complete system on paper.
Benefits
- Long processes run end to end. Work that crossed four systems and three people can complete without stopping.
- Each step done properly. Specialised agents handle their own step better than one agent attempting everything.
- Failures are locatable. Explicit handovers and one observation point turn a mystery into a diagnosis.
- You avoid building the wrong thing. The most valuable outcome of the review is frequently a decision not to proceed.
Technologies we use
- Orchestration. Coordinating which agent acts when, and what each receives from the last.
- Handover contracts. Defining exactly what passes between agents so a change in one does not silently break the next.
- Observability. Tracing a single piece of work across every agent that touched it.
- Failure and rollback handling. Deciding what happens when a later step fails after an earlier one has already acted.
- Checkpoint design. Placing human approval where the cost of a wrong action justifies the delay.
The engineering here is mostly about failure. Deciding what happens when step three fails after step two has already acted is harder than making the happy path work.
How the process works, step by step
Multi-agent work usually takes eight to twelve weeks, and it begins with an assessment that may recommend against it.
-
Step 1
Free process review
We map the process and establish honestly whether a single agent would do the job.
-
Step 2
The recommendation
We tell you whether to proceed. For most enquiries the answer is a single agent, and we say so.
-
Step 3
One agent first
The most valuable step is built and proved alone before anything is connected to it.
-
Step 4
Handovers added
A second agent is connected with an explicit contract and failure handling, then observed under real load.
-
Step 5
Extended carefully
Further agents are added one at a time, because complexity grows faster than the agent count.
Quality assurance and how we work
The commitments attached to building something this complex.
- We tell you when you do not need this. Most businesses asking for a multi-agent system need one agent doing one thing properly, and we say so at the review.
- We build one agent at a time. Each is proved under real load before the next is connected. Designing the full system on paper first is how these fail.
- Failure handling comes before the happy path. What happens when step three fails after step two acted is decided during design, not discovered in production.
- Human checkpoints where error is expensive. Full autonomy across a long chain is available and rarely advisable, and we will argue against it.
- We report when it is not working. If the system proves harder to run than the process it replaced, we say so rather than adding more agents.
Frequently asked questions about Multi-Agent systems
Do we need a multi-agent system?
Probably not. Most businesses asking for one need a single agent doing one thing properly, and we say so at the review stage.
Why do you argue against your own service?
Because the complexity is real. We have talked more clients out of this than into it, and an architecture whose main achievement is being hard to debug helps nobody.
When is it genuinely justified?
A long process crossing several systems, where a single agent has already been tried and proved insufficient. That last condition matters most.
Is three agents three times the complexity?
No, considerably more. Every handover is a place to fail, and cross agent failures are much harder to diagnose than failures inside one.
How do you handle a failure mid process?
It is designed before the happy path. What happens when step three fails after step two has already acted is decided during design, not discovered live.
Can it run fully autonomously?
Technically yes, and we will usually argue against it. Human checkpoints go where a wrong action is expensive.
How long does a multi-agent build take?
Eight to twelve weeks, beginning with an assessment that may recommend against proceeding.
Do you build it all at once?
No. One agent is proved under real load before the next is connected. Designing the full system on paper first is how these fail.
What if it does not work?
We report that rather than adding more agents. A system harder to run than the process it replaced is a failure, whatever it cost.
Can we start with one agent and grow?
Yes, and that is the recommended path. Most clients find the single agent solved the problem.
How do we debug it?
Through one observation point tracing a single piece of work across every agent that touched it. Without that these become unmaintainable.
Can different agents use different models?
Yes, and matching the model to the step is one of the genuine advantages of separating them.
Who owns this internally?
It needs a technical owner. Multi-agent systems are not something a business team can run alone, and we will say so before starting.
Does it work with our existing systems?
That is what makes it worth doing. Crossing several systems is the case where a single agent struggles most.
What happens when one agent is updated?
Handover contracts define exactly what passes between agents, so a change in one does not silently break the next.
Is this the same as workflow automation?
No. Workflow automation follows fixed rules. These agents handle steps requiring interpretation, which is also why they fail differently.
Can we see what each agent did?
Yes, per step, which is the only practical way to understand why an outcome happened.
Is this proven or experimental?
The individual agents are proven. Coordinating many of them reliably is genuinely harder, and we would rather you heard that from us.
Is our process data confidential?
Yes. Work passing between agents stays inside your own infrastructure, and no public AI model is trained on any of it.
What does a multi-agent system cost?
Agent count and integration complexity determine it, quoted after the review, which may recommend a cheaper single agent instead.
Glossary of terms
Terms used in multi-agent design.
- Orchestration
- Coordinating which agent acts when, and in what order.
- Handover
- The pass of work from one agent to the next, and the most common point of failure.
- Observability
- The ability to trace one piece of work across every agent that handled it.
- Checkpoint
- A point where a person approves before the process continues.
- Rollback
- Undoing earlier steps when a later one fails, which is rarely as simple as it sounds.
- Agent scope
- The single defined job one agent is responsible for.
Related services in AI Agents
Talk to us about Multi-Agent systems
Tell us the process you have in mind. If one agent would do it, we will say so and the conversation gets cheaper.
Start your project