Build
1/3
of agentic implementations will combine agents with different skills by 2027.
Gartner, August 20251Multiple agents are an operating design, not a sign of sophistication.
Companies add specialist agents because the architecture diagram looks modern. Then nobody can tell which agent failed, who owns the case, or what the run costs.
How I see it
Multi-agent systems
A multi-agent system can be the right answer when one outcome needs distinct skills, tools, or risk levels: research, action, review, and exception handling. It is the wrong answer when a single agent with a few tools would complete the case.
The design problem is coordination. Each agent needs a job, a contract, and a way to hand work forward without losing the case identity. The system needs one owner for the outcome, even if several agents contribute.
Cost and failure modes multiply. Every extra agent is more model calls, more retries, more places to hallucinate a status, and more monitoring. The economics have to pay for that complexity.
Start with one agent. Split only when a second responsibility is clearly different and is making the first agent worse.
Common mistakes
What teams usually get wrong.
A swarm with no case owner
If five agents can touch the work and none is accountable for done, you built a committee.
Duplicating the org chart in agents
Sales, ops, and finance agents that re-create departmental handoffs recreate the original delay.
No shared evaluation
Scoring agents separately can hide that the end-to-end case is still failing.
A useful diagnostic
Five questions before you fund the work.
Can one agent with more tools complete the outcome?
If yes, do not split the system yet.Are the jobs actually different, or just different prompts?
Prompt variants are not a multi-agent architecture.Is there one case identifier from start to finish?
Without it, you cannot operate the system.Do you know the cost per completed case with all agents included?
If not, the design is not ready for production volume.Who decides when the multi-agent run should stop?
Unbounded collaboration is a cost and quality problem.
Economic model
Coordination costagents × steps × model calls × retries × review rate = run cost before value
If coordination cost approaches the value of the case, collapse the design.
Three credible paths
How far should you go?
Do not force one solution. Choose the path the economics, the risk, and the organization can support.
Keep a single agent
Add tools and tighter instructions before adding more agents.
The outcome is one case with a clear done state.
A single agent can get clumsy if the tool set becomes too broad.
Split research from action
Let one agent gather context and another take write-back actions under tighter controls.
The risk of action is much higher than the risk of research.
You now have a handoff to design and monitor.
Add a reviewer agent
Use a second agent to check completed work before a human or a system accepts it.
Error cost is high and the review criteria can be made explicit.
Reviewers can agree with fluent mistakes unless evaluation is grounded in cases.
When this is the wrong next step
Do not fund an agent here.
- The first single-agent workflow is not yet in production.
- The team cannot explain the job of each agent in one sentence.
- Run cost is already high and the company has not measured it.
A useful next step
Bring one workflow. Get guided into production.
We guide the implementation, go deep on the technical path, and stay hands-on through operations — or tell you when a simpler answer is better.
Discuss an AI opportunity