Account research and preparation
Gather information from approved sources, identify gaps and produce a cited briefing for a colleague to review before a conversation.
AI agents.
Useful work, with boundaries.
Some jobs need more than a single answer. We build AI agents that can investigate a task, use approved tools and prepare the next step, with clear limits on what they can do and when a person must decide.
Explore an agent project- A defined job to do
- Controlled access to tools
- Approval where it matters
The impressive part is easy to demonstrate. The dependable part takes design.
An agent may choose which source to inspect or which tool to use as it works towards a goal. That flexibility can help with variable tasks, but it also introduces more ways to get an answer or action wrong. We begin by checking whether a simpler workflow would do the job.
When an agent is justified, we agree its responsibilities, tools, budgets and stopping conditions. Researching an account is different from emailing that account. Preparing an update is different from writing to a live system. Those boundaries belong in the software, the tests and the handover.
Give the agent a job you can evaluate.
Operational exception investigation
Look up the records behind a failed workflow and assemble an explanation, with proposed next steps routed to the responsible person.
Support investigation
Consult product guidance and permitted account information to prepare a response. Keep decisions about credits, promises and account changes behind approval.
Document-led task coordination
Check an incoming request, identify missing evidence and prepare a structured task list using the tools and actions agreed for the project.
Investigate an exception. Present a decision.
The starting point
An operations colleague opens a failed order, searches messages, checks two systems and writes a summary for a manager.
A better way forward
An agent collects the permitted records, flags inconsistencies and prepares a proposed resolution. The manager sees the evidence before approving any change.
- 1
Inspect
Use restricted read tools to gather the information relevant to the case.
- 2
Prepare
Produce a structured recommendation and show uncertainties or missing evidence.
- 3
Approve
Require a named person to authorise consequential changes and record the outcome.
An illustrative engagement. We agree the actual scope, access and success measures with your business.
Something useful.
Yours to put to work.
The engagement has a clear scope and a tangible handover. We agree the deliverables before work starts.
Discuss the scopeAn agent specification
Its task, tools, allowed actions, approval rules, run limits and explicit exclusions.
An integrated pilot
A working implementation with the agreed systems, access controls and review interface.
A failure-focused evaluation
Representative cases covering bad inputs, unavailable tools, misleading instructions, duplicate events and incomplete work.
Operational controls
Run records, cost monitoring, an interruption path and a documented process for changing or retiring the agent.
How we work with you.
Define the job
Choose a bounded task and compare an agent with a conventional workflow.
Limit the tools
Grant only the information and actions needed for that task.
Challenge the pilot
Test successful cases, ambiguous requests, failure recovery and approval bypass attempts.
Review real use
Start with supervision and agree changes using observed quality, cost and completion data.
A few things
you might be wondering.
Questions about your own setup?
Let’s talk it through
What is an AI agent?
An AI agent uses a model to decide some of the steps or tool calls needed to complete a task. Definitions vary, so we describe the actual behaviour and permissions in your project rather than relying on the label.
How is an agent different from workflow automation?
A conventional workflow follows steps defined in advance. An agent can choose some steps dynamically. Many useful systems combine both: a fixed process for approvals and record updates, with an AI component for investigating or drafting.
Can an agent take actions in our CRM or other software?
Potentially, where the software provides suitable access. Read and write tools are scoped separately. We define which actions are allowed, which need approval, and how to prevent duplicate changes before connecting to live records.
Can we start with a supervised agent?
Yes. A useful first scope can be read-only investigation and draft recommendations. Your team can review each output while we learn whether the system performs reliably enough to expand its responsibilities.
What happens if it cannot finish a task?
The agent should stop within agreed time, cost or step limits and return the available evidence with a clear incomplete status. We plan who receives that handover and which actions, if any, were already taken.
What determines AI agent development cost?
Tool integrations, task variability, access controls, evaluation coverage and the operating model are major factors. We distinguish build costs from usage and support, and agree a limited pilot before a wider rollout.
A little clarity before the next step.
Start with one job worth doing better.
Tell us what you’re working on. We’ll help you find a practical next step.
Explore an agent project