Services
An agent that can act on your systems is only useful if you can bound what it touches and replay what it did. We build the tools, the permissions, and the traces first, because that is what a security review actually asks about. The autonomy is the easy part.
Autonomy demos well. Accountability is what blocks the rollout.
An agent holding broad credentials is a security review that will never pass, however well it performed in the demo.
When an agent gets something wrong and nobody can reconstruct the steps, the only remedy available is switching it off.
Timeouts, changed schemas, partial writes — the real failures are in the tool calls, which is exactly where nobody is looking.
Without step caps and hard spend ceilings, one bad plan runs until billing notices, and billing is the wrong alarm.
Bounded, observable, and reversible before anything is autonomous.
Every capability is an explicit, tested tool with its own credentials and its own blast radius — not one shared key with everything behind it.
Anything irreversible, costly, or customer-facing pauses for a human decision by design, rather than by a policy somebody is trusted to remember.
Inputs, every tool call and its result, the reasoning at each step, and the output — recorded so a run can be audited and run again.
Step caps, timeouts, and per-run spend ceilings enforced by the runtime. A prompt asking an agent to be careful is not a control.
Two representative slices of a typical build.
Reads the ticket, looks up the account, applies your policy, drafts the action — and stops for a human on anything that moves money or cannot be undone.
One agent gathers, a second verifies claims against sources, a third drafts — each with its own tools and its own permission boundary rather than a shared pool of access.
Resolve routine tickets, escalate the rest with context intact.
Gather, verify, and draft — with the sources attached.
Agents for the back office, scoped to what each role may reach.
Separate roles only where a real permission boundary needs one.
Explicit, tested tools with their own credentials each.
Gates on anything irreversible, costly, or customer-facing.
Runs you can replay, step by step, months later.
Caps and ceilings enforced by the runtime, not requested.
The gate before the build, and the guardrails before the autonomy.
If the path is fixed and the inputs are predictable, deterministic automation is cheaper, faster, and far easier to audit. We say so when that is the answer.
Each tool gets its own credentials, its own tests, and its own failure behaviour, because the seams are where agents actually break.
Which actions need a human, and the step, time, and spend ceilings — agreed with you and then enforced in the runtime.
Live on a slice of real work with traces reviewed weekly, and scope extended only where the run history earns it.
Directional outcomes from comparable builds — not contractual targets.
Runs you can replay
Every action reconstructable, which is what makes sign-off possible.
Risk that stays gated
Irreversible steps keep a human decision in the loop by design.
A bad plan stays cheap
Step and spend ceilings are enforced by the runtime, not requested.
A chatbot answers. An agent acts — it calls your systems, chains several steps together, and finishes a task. That single difference is why permissions, approval gates, and run traces matter far more here than they do for a conversational interface.
Four things, in layers: scoped credentials so it can only reach what its job needs, an explicit allowlist of tools rather than open access, approval gates on anything irreversible or costly, and hard ceilings on steps, time, and spend per run. The last one is enforced by the runtime — a prompt asking an agent to be careful is not a control.
Every run is traced: the inputs, each tool call and its result, the decision taken at each step, and the final output. Without that you cannot debug an agent, and you certainly cannot audit one — which is usually the question that decides whether it ships.
More often than the market suggests. If the path is fixed and the inputs are predictable, deterministic automation is cheaper to build, faster to run, and far easier to audit — an agent there adds cost and non-determinism for nothing. Agents earn their place when the route genuinely varies per case. We would rather tell you that in week one than bill for the wrong architecture.
Usually not at first. One well-scoped agent with good tools beats a committee of them, and every extra agent adds coordination failure modes that are hard to trace. We add a second when a genuinely separate skill or permission boundary justifies it, not because the diagram looks better.
Agents are where those obligations bite hardest, because the system takes actions rather than producing suggestions. The recurring requirements — meaningful human oversight, logging, and traceability of automated decisions — are the same things approval gates and run traces exist to provide, so the engineering and the compliance answer point the same way. We build to the controls your compliance function specifies; we do not act as it.
A narrow, well-scoped agent on a handful of tools is a much smaller build than a multi-agent system with governance and its own observability stack, and published market ranges span that whole gap. The honest driver is how many systems it must touch and how strict the approval and audit requirements are, so we scope those before quoting.
Tell us the task. We will tell you honestly whether it wants an agent or plain automation.