An AI agent development company for agents that act, not chat.
As an AI agent development company we build custom AI agents that do things: call tools, change records, escalate to a person, and stop when the job is done. An agent without permissions, an audit trail and a definition of done is a chatbot with a larger blast radius, and we will not ship one.
The useful distinction is not agent versus chatbot, it is judgement versus flow. If the steps are known in advance, you want a workflow — cheaper, faster, debuggable, and it will still be running in three years. See automation. You need an agent when the path genuinely varies with the input: when the next action depends on what the last one returned, and enumerating the branches in advance is what is impossible.
When we build AI agents for business use, the model is the smallest part of the design. The engineering is in the tool surface — what the agent may call, with what arguments, under whose authority, and what it may never do. Every tool is a typed contract with validation, rate limits and reversibility, so the worst case is a rejected call rather than a corrupted record. Permissions are per-agent and least-privilege, because an agent inherits the blast radius of whatever credentials you hand it.
Then there is termination and observability, which is where most agent projects quietly fail. Agents need a definition of done, a step budget, a cost ceiling and a loop-detection path, or they will spend your money proving they cannot finish. Every run leaves a complete trace — inputs, tool calls, decisions, outputs — because when an agent does something surprising, "we cannot reconstruct what happened" is not an acceptable answer to a customer or an auditor.
Multi agent systems come up early in most conversations, usually too early. Splitting a task across specialised agents is genuinely right when it has distinct phases with different tools and failure modes, and it multiplies cost and debugging difficulty when it does not. We start single-agent and split on evidence, and we will tell you when multi agent AI is being proposed because it is interesting rather than because your task needs it.
The commerce context is where our twenty years actually help: agents that touch orders, stock, price and fulfilment run into systems where the ERP owns the truth. We have built that boundary repeatedly — SAP-backed logic across MASCOT Workwear's 20+ markets and TTI's 18 regional sites.
The parts that decide whether an agent can be trusted in production.
Tool surfaces as typed contracts
Every capability an agent has is an explicit, validated, rate-limited and where possible reversible interface. Broad database or shell access is not a tool, it is an incident waiting for a trigger.
Least-privilege permissions
Per-agent credentials scoped to the job, with destructive actions gated behind human approval by default. The agent inherits exactly the authority you decided to give it, and no more.
Termination and budgets
A definition of done, a step budget, a cost ceiling and loop detection. An agent that cannot finish should stop and say so, not keep spending.
Full-trace observability
Every run reconstructable end to end — prompt, tool calls, arguments, results, decisions. This is what makes debugging and audit possible, and it has to be built in from the start.
Multi-agent only where it earns it
Distinct phases with distinct tools and failure modes justify a split. Anything less and a single well-instrumented agent is cheaper to run and far easier to reason about.
Evaluation harness before feature
A golden set built from your own queries and records, scored automatically on every change and wired into CI as a release gate. It is what makes every later claim checkable — including ours.
How an agent build runs.
Decide whether it needs to be an agent
We map the task. If the branches are enumerable, we recommend a workflow instead and the project gets cheaper. This is a real outcome, not a rhetorical device.
Design the tool surface first
Before any prompt: what the agent can call, what it must never do, what needs approval, and what a rejected call looks like.
Evals, then a narrow live pilot
A golden set of real tasks with graded outcomes, then production on a slice with a human gate and full tracing.
Widen on evidence, hand over
Scope grows where the trace data supports it. Runbooks and architecture decisions land in your repository as we go.
Engineering standards, not client percentages.
No agent accuracy percentages here, because a number without the task distribution behind it is decoration. In an engagement, accuracy is defined against your golden set and reported against it.
The stack.
Common questions.
When do we need an agent rather than a workflow?
When the path varies with the input in ways you cannot enumerate. If you can draw the flowchart, build the flowchart — it is cheaper, faster and debuggable. We turn down agent projects that are really workflow projects, because they fail in production and neither of us wants that reference.
How do you stop an agent doing something destructive?
By making it structurally impossible rather than unlikely. The agent can only act through typed tools you approved; destructive operations require human confirmation by default; credentials are least-privilege per agent; and every call is validated, rate-limited and logged. Prompt instructions are the weakest control available and we do not rely on them for safety.
Do you build multi agent systems?
Yes, when the task has distinct phases with different tools and failure modes. But we start single-agent and split on evidence — multi agent AI multiplies cost, latency and debugging difficulty, and a lot of multi-agent architecture is proposed because it is interesting rather than because it is needed.
Which framework do you use?
Deliberately as little as possible. Agent frameworks move fast and abstract exactly the things you need control over — tool contracts, retries, tracing, cost. We build on the model provider's tool-calling API with our own thin orchestration, using MCP where a standard tool interface helps. That keeps you portable across models.
Can an agent work with our ERP and order systems?
Yes, and that boundary is the interesting part of the project. Where the ERP owns price, stock and fulfilment truth, the agent gets read access plus a small set of write tools with approval gates. We have built ERP-owned boundaries repeatedly — see ERP integration.
Do you have agents running in production for clients?
We would rather answer that specifically on a call than in marketing copy, because the useful version of the answer names systems and constraints. What we will not do is publish an AI case study we have not agreed with a client, or quote a metric with nothing behind it.
Where to next.
Describe the task, not the architecture.
Tell us the job you want done and what systems it would touch. We will tell you whether it needs an agent, a workflow, or better data — and what the tool surface would have to look like.