Agents that are accountable.
An agent is only as good as what it is allowed to see, what it is allowed to do, and how you find out when it is wrong. I build agentic AI products with those three answered before the first prompt is written.
- Grounding
- Retrieval with citations, not recall
- Guardrails
- Evidence gates, typed tool outputs
- Resilience
- Multi-provider fallback
- Operations
- Docker, CI/CD, logs, metrics
What makes an agent production-grade
The same discipline that made five AI applications ship.
- 01
Ground every step
The agent retrieves before it reasons. Hybrid search and re-ranking give it evidence; citations make its work checkable.
- 02
Type the tools
Every tool call and every extracted field passes a schema. A model never writes free text into a system that acts on it.
- 03
Gate the output
A confidence gate scores the evidence; weak support means the agent stops and says why, rather than improvising.
- 04
Plan for the outage
Provider failures fall back across models with identical prompts, so the product keeps working when one API does not.
- 05
Measure, then ship
Base-versus-tuned comparisons, test suites that run on every change, structured logs and metrics from the first deploy.
Built on
The production systems these patterns come from.
Read next
Deeper on the pieces.
An agent your users can rely on?
Bring the workflow; I will tell you where the evidence, the tools and the gates go.