CTOs and VPs of Engineering
Build / AI agent development
AI agent development for real work
Design and implement bounded agents that can reason, use tools, request approval, recover from failure, and leave an inspectable record.
Move from an impressive chat demo to a controlled system that completes a named job inside explicit authority, quality, cost, and latency limits.
When to call
These are useful signals that the next decision needs more than another tool, vendor demonstration, backlog item, or workshop.
- A prototype works on happy-path prompts but fails on real cases
- The agent can call tools, but nobody can explain its permissions
- Quality is judged by demos instead of a repeatable task suite
- Failures are expensive, silent, or difficult to reconstruct
- The model is being asked to do work that should remain deterministic
The outcomes
- A bounded workflow with clear autonomous, deterministic, and human steps
- Tool contracts, identity, least-privilege permissions, and approval points
- Representative evaluations for task success, safety, cost, and latency
- Traceable execution, failure handling, and operating ownership
- A production decision supported by evidence rather than model enthusiasm
What leaves the engagement
The exact artifact set is scoped to the decision, but the intended result is working behavior, visible evidence, and an owner—not a report that cannot be operated.
- Use-case and autonomy-boundary map
- Agent, tool, data, and approval architecture
- Threat model and failure-mode register
- Evaluation dataset and acceptance thresholds
- Working production slice with traces and runbooks
- Rollout, monitoring, rollback, and ownership plan
How the work proceeds
- Frame the job. We define the user, trigger, decision, systems touched, cost of error, escalation path, and evidence required. If a rules engine or conventional automation is the better answer, we say so.
- Constrain the system. We separate reasoning from execution, minimize tools, scope credentials, validate inputs and outputs, and place human approval before consequential actions.
- Evaluate the workflow. We test realistic tasks, adversarial cases, tool failures, long-context behavior, retries, and economics. A model score alone is not a workflow acceptance test.
- Release and operate. We deploy incrementally, capture traces without leaking sensitive data, monitor quality and spend, and define how the team pauses, investigates, and changes the system.
Limits that stay explicit
Serious implementation work includes the conditions under which its claims do not hold.
- An agent is not a reliable substitute for missing process ownership or undefined business policy.
- Guardrails reduce risk; they do not make probabilistic output infallible.
- High-impact decisions may require mandatory human review, legal analysis, or domain-qualified oversight.
- Provider capabilities, pricing, and retention controls must be rechecked at implementation time.
Questions teams ask
When should we build an agent instead of an automation?
Use an agent when the task genuinely requires interpretation, planning, or adaptation across variable inputs. Use deterministic automation for stable rules and repeatable transformations. Many reliable systems deliberately combine both.
Can you work with our existing model and cloud providers?
Yes. We start with the constraints and evidence required, then fit the architecture to approved providers, data boundaries, identity systems, and operational tooling.
Do you guarantee that an agent will never make a mistake?
No credible implementation team can make that guarantee. We reduce the probability and impact of failure with boundaries, evaluations, approvals, monitoring, and safe recovery.
AI agent development / first decision
Bring the real constraint.
Tell us the current state, the outcome that matters, and what has already been tried. The first conversation is for fit and truth—not a promise made before the system is understood.
Please do not send secrets, credentials, regulated data, or confidential customer material through an initial inquiry.