AI agent development for workflows that have to be right.
An agent that drafts a summary can be wrong and waste a minute. An agent that touches your systems can be wrong and cost you a customer. The engineering difference between those two is the whole job.
- An agent demo that works until the input is slightly unusual.
- No clear answer to what happens when a tool call fails halfway.
- Nobody willing to grant it write access to anything that matters.
- Costs that swing wildly because the loop runs until it gives up.
What’s included
Workflow selection
Which processes suit an agent, and which are better served by a deterministic pipeline.
Tool design
Narrow, well-described, hard-to-misuse tools — the largest single lever on agent reliability.
Approval gates
Human review placed exactly where an irreversible action would otherwise happen unattended.
Failure handling
Retries, timeouts, and compensating actions for the half-finished run, which is the normal case.
Observability
Full traces of what the agent decided and why, because debugging without them is guesswork.
Cost and loop control
Step budgets and termination conditions, so a confused agent stops instead of spending.
How we work
- Prove3–4 weeks
Production PoC
Build one use case for real — and measure it.
- One use case built production-grade
- Evaluated against a real KPI
- Evidence-based go / no-go
- Ship6–12 weeks
Build & Integrate
Put the system into production — integrated and compliant.
- Full build, integrated with your stack
- Security & compliance built in
- Live, monitored, documented
- ScaleOngoing
Scale & Enable
Expand across workflows — and level up your team.
- Rollout across teams and workflows
- Evals & monitoring in place
- Team enablement + fractional AI leadership
Reliability is a function of tool design
Teams tend to attack agent unreliability at the prompt. The larger lever is the tools. An agent given one broad tool with eight optional parameters will misuse it; the same agent given four narrow tools that each do one thing, with descriptions that state their preconditions, will not.
Design tools the way you would design an API for a competent contractor who cannot ask questions. Make illegal states unrepresentable. Return errors that say what to do differently rather than what went wrong internally. Keep the parameter surface small enough that the right call is the obvious one.
This is ordinary interface design, and it is why the work suits engineers rather than prompt specialists. The prompt is the least durable part of an agent; the tool boundary is the part that determines whether it can go wrong at all.
Approval gates and blast radius
The question that decides an agent’s architecture is what it is allowed to do without asking. Reading is cheap to get wrong. Writing to a customer record, moving money, or sending an external message is not, and those deserve an explicit gate.
In practice that means separating the plan from the execution, presenting the plan where a person can reject it, and scoping credentials so the agent physically cannot reach systems outside its remit. An agent with broad write access and no gate is not an automation, it is an incident waiting for a trigger.
Agents fail differently than chatbots
A chatbot’s failure is a bad answer, visible immediately to the person who asked. An agent’s failure is a half-completed sequence of real actions, discovered later by someone who was not watching.
That changes what you build. Every run needs a trace you can replay. Multi-step work needs compensating actions for partial completion. And the loop needs a hard stop, because the characteristic agent failure is not a wrong answer but an expensive refusal to conclude.
- Are agents ready for production work?
- For well-scoped workflows with good tools and gates on irreversible actions, yes. For open-ended autonomy over critical systems, not yet. Deciding which side a workflow falls on is the first thing we do.
- What makes a workflow a good candidate?
- Clear success criteria, tolerable cost of being wrong once, a bounded set of systems to touch, and enough volume to be worth automating. Workflows failing several of those are better served by a deterministic pipeline.
- How do you stop an agent doing something destructive?
- Scoped credentials so it cannot reach what it should not, approval gates before irreversible actions, and step budgets that terminate a run. Prevention sits in the architecture, not in the instructions.
- Can agents work with our internal systems?
- Yes — that is usually the point. Tools wrap your existing APIs and services, which is also where the reliability work concentrates.
- How do you control the cost?
- Step budgets, termination conditions, model choice per step, and caching. Runaway cost is nearly always an uncapped loop rather than an expensive model.