Agent engineering · in public
I build AI agents that know when to ask a person.
Agents that do real work with real tools. Evals decide whether a change ships, and audit logs, redaction, and red-team cases are part of the build. I came to agents from security, so a wrong approval counts as far worse than a needless question.
- Test invoices right
- 155/158
- False approvals
- 0
- Cost per 100 invoices
- $0.19
- Median time per invoice
- 8.7 s
Build log
- ap-agentClaude Haiku 5.5 decided 155 of 158 held-out test invoices correctly, with zero false approvals.
- evalsHaiku 4.5 and Haiku 5.5 both decide 155 of 158 test invoices correctly with zero false approvals.
- audit-controlsAll 18 red-team invoices in the test split end in their required outcome.
- mcp-toolsThe full test split, replayed with every ERP tool served over MCP, hit all 158 recordings and scored the same 155 of 158, with no row different from the in-process run.
- knowledge-layerOn 42 test invoices whose answer depends on the firm's contracts and notes, the agent went from 12 right without knowledge to 36 with it, with zero false approvals.
- voice-receptioniststarted
- evalsstarted
- knowledge-layerstarted
- slack-reviewTested live in a real workspace.
- slack-reviewstarted
- audit-controlsstarted
- mcp-toolsstarted
Agent engineering
Most of this work is one product, an accounts-payable agent, built a layer at a time. Its source is private for now, so every row is marked building: you can read what it measured and when, but you cannot clone it yet.
Agents & Tool Use
Evals
Guardrails & Audit
Integrations
Security
Where I started. The lab is in build, and 8 more projects are planned, each with the bar it has to clear.