An AI agent is more than a chat box. It can plan a bounded task, use approved tools, inspect results, and ask for help when a decision exceeds its authority. We build agent workflows around explicit permissions, observable steps, and useful failure behaviour.
For teams exploring multi step AI workflows where a fixed prompt or simple automation is not enough.
Related reading: our study of 56 UK accountancy websites, covering AI crawler access, structured data and response times.
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Some workflows need more than a fixed sequence: reading a situation, choosing which tool to use, and deciding when to ask a person. That is an agent, and it is right for a narrow set of problems.
Bounded agents with typed tools and approval points can take on work that a simple automation cannot, without the failure modes of an unbounded one.
Most businesses that think they need an agent need a fixed automation. Knowing the difference is the first thing we check.
Define the task, tools, data boundary, success criteria, refusal conditions, and human handoff before selecting a model.
Keep actions narrow and inspectable. Validate tool arguments, isolate credentials, and require confirmation for consequential operations.
Evaluate with representative cases, adversarial inputs, latency, cost, and real failure logs before expanding access.
A good process makes the work easier to understand, easier to measure, and easier to improve.
We start with the goal, audience, constraints, existing stack, and the result that would make the project worthwhile.
We turn the brief into a focused plan, with clear priorities, technical decisions, responsibilities, and measures of progress.
We design, implement, and test the work against real devices, real data, accessibility requirements, and the edge cases that matter.
We release carefully, watch the evidence, and use what we learn to improve performance, visibility, and the next useful iteration.
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Explore the serviceWhen a workflow needs judgement between steps: reading a situation, choosing a tool, deciding when to ask a person. That is a narrow set of problems, and most businesses that think they need an agent need a fixed automation, which is cheaper and more predictable. When an agent is right, it is right with typed tools, retrieval, evaluation cases and a human approval point, and we build nothing else.
An AI agent is a model assisted workflow that can interpret a task, choose from approved tools or steps, inspect results, and continue or escalate within defined limits.
A fixed automation follows known rules. An agent can handle some variation and select the next permitted step, but that flexibility adds testing, permission, monitoring, and cost requirements.
It can use narrowly scoped tools when the access model is designed correctly. Credentials should remain outside the model, actions should be validated, and sensitive operations should require appropriate approval.
Use least privilege, allowlisted tools, typed inputs, step limits, data filters, confirmation gates, audit logs, evaluation cases, and a reliable human escalation route.
The best model depends on task difficulty, latency, cost, context, privacy, and tool use. The workflow should be evaluated against representative cases rather than choosing on brand name alone.
Have a specific brief, dataset, or existing system in mind?