Evidence-based report preparation
Allocate source collection, structured analysis and editorial checking to separate components, while retaining source references and flagging unsupported conclusions for a reviewer.
AI agents & knowledge
AI agents & multi-agent systems
Coordinate specialist agents to assist with complex, multi-step business tasks.
Built around a specific business need.
Some business tasks combine several kinds of work: finding evidence, applying specialist rules, checking an output and preparing a decision. A multi-agent system separates those responsibilities into coordinated components. That separation is useful only when it makes the task easier to control and evaluate; adding more agents does not automatically create a better solution.
Kent Consultancy would assess whether distinct specialist agents are justified, then define the information each receives, the tools each may use and the conditions for passing work onward. A coordinating workflow would make progress visible and preserve an accountable route to human review. The design would favour explicit responsibilities over open-ended conversations between agents.
Who this is for
- Teams handling complex tasks with several clearly distinct disciplines.
- Organisations that need separate preparation, checking and approval stages.
- Technical teams exploring agents beyond a single assistant or workflow.
Where this can make a practical difference.
Operational case assembly
Coordinate agents that retrieve records, check required fields and prepare a case summary. Keep the final decision with the named case owner.
Specialist task routing
Direct work to an appropriate specialist component based on explicit requirements, then consolidate results without allowing one agent’s assertion to become unquestioned evidence for another.
How we approach the work.
Decompose the task
Map inputs, outputs and dependencies. Compare a multi-agent design with a simpler workflow and retain specialist agents only where they offer a defensible operational benefit.
Define contracts between agents
Specify structured output formats, provenance and completion conditions. Distinguish facts retrieved from systems, model interpretations and unresolved questions so later stages can treat them appropriately.
Constrain coordination
Set allowed handovers, maximum iterations, time budgets and failure states. Prevent circular delegation and make it possible to pause or cancel an incomplete run.
Separate permissions
Give each agent only the data and tools required for its role. Require human approval at consequential boundaries rather than relying solely on another model to approve a model.
Evaluate the whole chain
Test individual components and complete task runs. Trace where errors originate, whether checking catches them and how the system responds when a specialist produces incomplete work.
The sequence is tailored to the agreed scope, dependencies and risk. It describes our delivery approach, not a fixed timeline or performance guarantee.
What the engagement can deliver.
- An architecture and responsibility map.
- Agent instructions, interfaces and scoped tool access.
- Coordination logic with explicit stopping conditions.
- Component and end-to-end evaluation cases.
- Run tracing and human review procedures.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
End-to-end correctness
Completed tasks meeting the full acceptance criteria after review.
Error containment
Faulty intermediate outputs identified before consequential action.
Resource use per task
Time, model usage and review effort across the complete run.
These are measures to discuss and baseline. Targets are agreed for the specific system and data; no universal improvement percentage is assumed.
What to prepare for discovery.
- A task with separable responsibilities and agreed acceptance criteria.
- Representative examples, including failed or disputed cases.
- Owners able to approve tool permissions and review outcomes.
Questions worth asking.
When is one agent enough?
When a task has a straightforward context and permission model, one agent or a deterministic workflow may be easier to maintain. Discovery should test that option first.
Can agents validate each other?
They can provide additional checks, but shared assumptions can lead to shared errors. Independent evidence, deterministic checks and human judgement remain necessary for consequential outcomes.
How do we investigate a failure?
A useful implementation records relevant inputs, tool results, handovers and decisions within agreed privacy limits, so the team can reconstruct the sequence and improve the responsible stage.