The system around the model
Reliable agents need orchestration, permissions, validation, and operations—not only a strong prompt.
Workflow orchestration
Explicit plans, state, retries, checkpoints, and resumable execution for multi-step work.
Safe tool use
Least-privilege tools, validated arguments, approval gates, and clear recovery for consequential actions.
Model and latency routing
The right capability tier for each step, with streaming, compact context, and cost controls.
Evals and observability
Scenario tests, traces, outcome metrics, failure review, and measurable release gates.
AI agent development, from workflow to production
What makes an agent useful, reliable, secure, and maintainable after the first successful demo.
Start with one complete business job
We map the trigger, inputs, decisions, tools, exceptions, approvals, and definition of done for a real workflow. That creates a bounded agent with measurable value instead of a general assistant that is difficult to trust.
- Research and knowledge workflows
- Operations and support tasks
- Sales and customer follow-up
- Software and data workflows
Reliability lives outside the prompt
Production agents need structured tools, application-side validation, durable state, permission boundaries, evaluations, and clear human escalation. We design those foundations together so a model update or edge case cannot silently change what the system is allowed to do.
Operate and improve from evidence
Traces connect the request, model decisions, tool inputs, outputs, approvals, and final result. Teams can review failures, measure completion and cost, and promote real incidents into permanent regression tests.