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.

See the work →devheadx@gmail.comOpen to AI agent engineering roles
Test invoices right
155/158
False approvals
0
Cost per 100 invoices
$0.19
Median time per invoice
8.7 s
An Accounts-Payable Agent · measured 2026-10-12 · source private for now

Build log

  1. ap-agentClaude Haiku 5.5 decided 155 of 158 held-out test invoices correctly, with zero false approvals.
  2. evalsHaiku 4.5 and Haiku 5.5 both decide 155 of 158 test invoices correctly with zero false approvals.
  3. audit-controlsAll 18 red-team invoices in the test split end in their required outcome.
  4. 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.
  5. 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.
  6. voice-receptioniststarted
  7. evalsstarted
  8. knowledge-layerstarted
  9. slack-reviewTested live in a real workspace.
  10. slack-reviewstarted
  11. audit-controlsstarted
  12. 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.

Detection & Response

All security work →