We build AI agents that take real action across your tools. They call your APIs, update your records and drive a multi-step job to the end, with logs, guardrails and a person in the loop where it matters. We build the whole product around the agent, and we run a multi-agent system on ourselves every day to ship software.
An AI agent, the building block of agentic AI, is a language model that takes actions instead of only producing text. It decides what to do next, calls the tools and systems you give it, and keeps going until the task is done. It's one part of our wider AI development services, and a different thing from a plain chatbot. Here's the line between them.
Given a goal, it plans the steps and takes real actions across your tools: it calls an API, updates a record, runs a workflow, and loops until the job is finished. It can pause for a person to approve the steps that carry weight, and it logs what it did. It acts on your systems, and it's the side this page is about.
Answers a question and stops. It replies from what it knows or from your data, but it doesn't touch your tools or change anything. It talks, which is a different kind of product. Plenty of products want both: an agent that acts and a grounded model that answers.
Agents that take real actions across your tools
An agent is only useful once it can do something. We wire the model to your systems so it can take actions (call an API, update a record, move a task forward) within the limits you set, then build the orchestration that runs the whole sequence reliably. We run one of these on ourselves: WolfAI, our internal delivery platform, includes an AI build pipeline that carries a multi-epic build through its stages on its own and posts progress as it goes. Building agents we depend on daily is what taught us where they need guardrails and where they need a human.
An AI that runs a whole task end to end
Some jobs a person used to do by hand can be handed to an agent outright. We built Lizzy AI, which runs live, AI-graded first-round interviews: the AI conducts the interview and grades it, so a screening step that used to take a recruiter's time now runs on its own. It's a shipped example of an agent that owns a task from start to finish rather than assisting with a piece of it.
Human-in-the-loop action and approval workflows
For anything that carries weight, the agent proposes and a person decides. We built this pattern into a healthtech platform where an AI extracts the data from a document and a staff member reviews it before it's accepted, so nothing lands in the record on the model's say-so alone. On the Roam AI ESP Platform the same pattern runs around the client's optimisation model, and our part was the interface. We redesigned the platform and built its new front-end, centred on a recommendations inbox where operators review what the model proposes and act on it. The model and the workflow behind it are the client's; our work was the front-end that keeps a person in control of the call.
Multi-agent systems and orchestration
When one agent isn't the right shape for a job, we build several that coordinate. WolfAI runs multi-agent web code audits, where specialised agents each take a pass and their findings come together. Orchestration is what keeps that dependable rather than chaotic, and it's why we build on tools like LangGraph and Temporal for the long-running, multi-step cases.
Workflow and process automation with a checkpoint
A lot of the value is quieter than a fully autonomous agent. It's taking a manual, repetitive workflow (data entry, document handling, routing) and putting an AI step in the middle with a human checkpoint. The healthtech flow above is one shape of this, and it's often the safest place to start, because you get the time back without handing over the final say.
Need a fully autonomous agent for a specific back-office process, an agent wired into a long list of the SaaS tools you already run, or grounding so an agent can answer from your own data? Those sit within our broader AI development services, and grounding a model in your data is what our LLM and RAG products do. Start there and we'll scope the right approach with you.
An assistant your team talks to that actually does the thing: files the ticket, updates the record, kicks off the workflow, instead of just explaining how.
Pull the fields out of incoming documents, check them, and route them, with a person reviewing the ones that matter before anything is committed.
An agent that works through a queue (applications, tickets, requests), scores or sorts each one, and hands the edge cases to a human.
Agents that gather information across your sources, pull it together and produce a draft a person signs off.
Agents that watch a system, notice when something needs doing, and either act within set limits or flag a person.
The review layer that turns a model's recommendations into decisions your team can accept, reject or override, with the audit trail to back it up.
01
Discovery: scope the job and the guardrails
02
Scope and estimate
03
Build the agent and the tooling around it
04
Evaluate, harden and launch
Discovery: scope the job and the guardrails
We run workshops to pin down exactly what the agent should do, which tools and systems it touches, and where a person has to sign off, before any building starts. Then we map the data and systems it will depend on, because AI quality starts with data quality. This is also where we agree that an agent can't be guaranteed right every time, and design for that.
Scope and estimate
We prioritise with a must / should / could breakdown, and model the ongoing API and inference costs upfront, because an agent that loops can multiply token cost if you don't plan for it. You get a clear estimate and a billing model that fits (time-and-materials or fixed), and we build only once the scope and the data are confirmed.
Build the agent and the tooling around it
We wire the model to your tools so it can take actions, and build the orchestration that runs the sequence reliably, reaching for LangGraph or Temporal where the workflow is long-running or multi-step. Around that we build the parts that make it safe to run: the logs, the guardrails, and the human review and approval screens. We benchmark the models and keep the engine swappable, so you're not locked to one provider.
Evaluate, harden and launch
We test the agent against real scenarios, including the failure cases, then harden it: the permissions, the rate limits, and the guardrails that stop it doing something it shouldn't. We ship to production and support the go-live, and for a workflow that keeps changing, we make sure the agent holds up on the messy real-world cases, not only the clean ones.
Because an agent acts on real systems, the stakes are higher than with a model that only answers: a wrong call can change a record, spend money or take a step that's hard to undo. The guardrails matter as much as the capability.
A person in control where it matters. Our default for any action that carries weight is that the agent proposes and a person approves. On the Roam ESP platform we designed and built the front-end where that happens: a recommendations inbox where operators review what the client's model proposes and act on it.
Logs and limits. An agent runs inside permissions, rate limits and guardrails that bound what it's allowed to do, and it logs what it did, so its actions can be checked rather than assumed.
Honest about uncertainty. We agree upfront that an agent can't be guaranteed right every time, design the workflow to handle the cases where it's unsure, and keep sensitive data under ISO 27001 practices and GDPR.
LangGraph
Temporal
OpenAI GPT
Anthropic Claude
PromptLayer
Python
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