AI Agent Development for Multi-Step Work

A workflow follows steps you define in advance; an AI step might classify or draft, but the path is fixed. An agent decides its own next step based on what it finds, choosing from the tools you give it. Workflows are cheaper and easier to test, so we use them wherever the path is predictable and keep agents for work where it genuinely varies.

Several layers. Each tool gets the narrowest access that does the job, read-only by default. Only listed actions are possible at all, irreversible ones need a person’s approval, and step, time and spend limits stop runaway loops. We also treat anything the agent reads — emails, web pages, documents — as untrusted, and test deliberately for instructions hidden inside them.

Anything that moves money, changes a contract, alters someone’s personal record, sends something in your name that has not been sent before, or cannot easily be undone. Decisions about people — hiring, credit, health or legal outcomes — stay with a person entirely: the agent can gather and prepare, never decide. We agree the list with you, and your compliance team, before build.

With a replay set built from your past cases: each one records what a good outcome looks like and which tools should have been used. We score every release against it, inject failures such as slow or broken tools, and then run the agent in shadow mode on live work — proposing actions that people carry out — before it is allowed to act on its own.

It stops and hands over, rather than guessing. The person picking it up sees what the agent was asked, what it tried, what it found and why it stopped, so they can finish the task without starting again. Frequent hand-offs on the same kind of task tell us where a tool, a rule or the instructions need work.

Often, yes. The options include read access to the database, scheduled exports, an email-based route, or asking the vendor about an API they do not advertise. Driving a screen like a person is a last resort because it breaks whenever the interface changes. We set out the trade-offs of each before anything is built.

Yes. A Model Context Protocol server exposes your system’s actions and data as tools that compatible AI clients and agents can use. We build them with the same care as any API: authentication, per-user permissions, input validation, rate limits and logging — so connecting a new AI tool does not quietly widen access to your data.

Both, depending on the job. Frameworks such as the OpenAI Agents SDK, Claude Agent SDK or LangGraph save time on state, retries and tracing. For simpler agents, a short loop in Python or TypeScript is easier to read and maintain. Either way the tools, permissions and tests are our own code in your repository, so the framework can be swapped later.

Yes. Every run is traced step by step — the request, the plan, each tool call with its inputs and results, and the final action — and kept for a retention period you set. That record answers customer complaints, supports internal audit and helps with UK GDPR accountability; your DPO decides how long personal data in traces should be kept.

A discovery sprint from £2,000 maps the task, the tools and the approval points, and ends with a fixed-price quote for a pilot. Running costs are mainly model fees, paid directly to the provider: agents make several model calls per task, so we estimate the cost per task during discovery and set hard spend limits in production.