Case preparation
Retrieve permitted records, identify missing information and prepare a proposed action plan. Pause when evidence conflicts or a decision exceeds the agent’s authority.
AI agents & knowledge
Agentic AI solutions
Build agents that plan and carry out defined tasks with appropriate human checkpoints.
Built around a specific business need.
A conventional assistant produces an answer. An agentic system can also choose from permitted tools, plan intermediate steps and act towards a defined objective. That creates opportunities to remove repeated coordination work, but also makes boundaries essential: an agent needs a clear definition of success, limited authority and a safe way to stop.
Kent Consultancy would focus on a specific operational task rather than broad autonomy. We would identify which steps genuinely require judgement, which should remain fixed rules and which need approval. The resulting design would expose planned actions and verified outcomes, allowing people to retain control without manually conducting every low-risk step.
Who this is for
- Operations teams handling repeatable tasks with variable intermediate steps.
- Product teams adding controlled action-taking to an existing assistant.
- Organisations ready to move from AI drafts to supervised execution.
Where this can make a practical difference.
Supervised system updates
Translate an approved request into a sequence of limited changes, showing the intended changes before execution and checking the resulting records afterwards.
Research-to-workflow support
Collect information from approved sources, assemble a structured brief and open a review task for the appropriate colleague without treating research findings as permission to act.
How we approach the work.
Bound the objective
Choose a measurable task and define completion, exclusions and escalation conditions. Agree the maximum effort the agent may spend before returning an incomplete result.
Design the tool boundary
Expose narrow operations with validated inputs. Separate read access from write access and avoid broad credentials that allow unrelated system changes.
Build the execution loop
Implement planning, tool selection, result checking and stopping rules. Treat tool responses and retrieved text as information, not as authority to change the agent’s instructions.
Add approval and recovery
Require review for consequential actions, preserve enough state to investigate failures and design compensating steps where possible. A partial run must not be presented as complete.
Evaluate before expansion
Exercise normal tasks, denied permissions, malformed inputs and unavailable tools. Start with limited authority and review evidence before considering additional actions or users.
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.
- A defined task and authority model.
- Agent configuration and restricted tool interfaces.
- Approval checkpoints and failure-handling logic.
- Evaluation scenarios with recorded outcomes.
- Monitoring guidance and an operational ownership plan.
The proposal identifies the deliverables, acceptance evidence, responsibilities and support arrangements included in your engagement.
Measure what matters to this workflow.
Verified completion
Tasks that satisfy the objective and pass outcome checks.
Approval accuracy
Proposed actions accepted without substantive correction by reviewers.
Boundary adherence
Tested attempts to exceed scope that are stopped or escalated.
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 observable outcomes and a manageable action scope.
- System owners able to provide limited test access.
- Agreed approval responsibilities and acceptable failure handling.
Questions worth asking.
How autonomous should the agent be?
Only as autonomous as the task evidence supports. A useful starting point is preparation and proposed actions, followed by narrowly authorised execution after review.
What happens if a tool fails halfway through?
The workflow should record completed steps, stop unsafe continuation and explain the partial outcome. Recovery depends on the connected systems and should be designed for the specific task.
How is this different from multi-agent AI?
Agentic describes planning and acting within an objective. Multi-agent describes a system with several coordinated agents. A task can be agentic with only one agent.