AI & Agentic Systems
AI experiments are not becoming dependable production systems. Models may perform in isolation while the surrounding workflow lacks reliable context, permissions, evaluation, and human control.
The commercial trigger
A model, copilot, or agent must operate inside a material business or regulated workflow, and its decisions must be observable, bounded, and recoverable.
Accountable technology leadership
Technology, product, and data leaders moving AI from demonstration into accountable production use.
Production AI applications and agentic workflows connected to real enterprise systems.
Agent and tool orchestration
Retrieval and enterprise knowledge pipelines
Model routing and structured outputs
Human review, escalation, and override paths
Evaluation, tracing, and production observability
Identity, permissions, and workflow integration
Consequence changes the technical work.
The hard part is rarely the model call. Reliability depends on changing data, nondeterministic behavior, tool failures, authorization boundaries, latency, cost, and knowing when the system should stop and ask a person.
Architecture through controlled release.
- Define decision boundaries and failure modes before selecting models
- Build evaluations around representative tasks, edge cases, and unsafe behavior
- Integrate tools through explicit contracts and least-privilege access
- Release with observable controls, human escalation, and rollback paths
Artifacts teams can build, operate, and govern.
Agent and tool architecture
Evaluation and observability harnesses
RAG and knowledge pipelines
Production integration and release controls
Where this practice carries particular consequence.
Healthcare & Life Sciences
Clinical support and operational workflows with accountable human decisions.
Explore industry →Financial Services & Insurance
Decision support and reviewable customer operations.
Explore industry →Legal & Professional Services
Knowledge work with confidentiality and review boundaries.
Explore industry →Comparable problems and system scope.
Explore the underlying capabilities.
Bring us the system, constraint, and consequence.
An engineer will assess the technical fit and the next useful decision.