A complete, runnable example of an Agentic PeopleOS agent — a Talent Acquisition reporting agent for a fictional company (Acme Corp). It reads open requisitions, computes the weekly operating report, drafts a Day-1 digest, and stops at a human publish gate.
It demonstrates, in one small agent, the principles the framework is built on:
- a defined identity (
SOUL.md) with immutable guardrails - a budget (
cost_tracker.json) and tiered model use (the report needs no model at all) - scoped tools (
tools.yaml) — read-only, with no "send" tool - human-in-the-loop — it produces a draft and a human owns the publish decision
- auditability — the same input always produces the same report
- an eval (
evals/test_report.py) that guards the logic
All data is synthetic. No real company, system, or person is represented.
A branded, self-contained HTML dashboard (output/report.sample.html)
plus a Day-1 digest. It opens with a data-derived insight ("what needs attention"), then KPIs,
pipeline / age / department breakdowns, a recruiter scorecard, risk flags, and a governance footer.
No dependencies — Python 3.9+ standard library only.
cd examples/ta-reporting
python3 run.pyThis writes the report and digest to output/ and stops at the publish gate. Then:
open output/report.sample.html # the operating report (macOS; use your browser)
cat output/day1-digest.sample.md # the digest a human reviewsTo see the gate enforce itself:
python3 run.py --publish # refused — needs a named approver
python3 run.py --publish --approved-by "Dana Lopez" # records the human approvalpython3 evals/test_report.pyThe eval covers the happy path plus the fail-closed data contract (missing / empty / malformed / schema-invalid input) and the publish gate's exit codes.
See SPEC.md for the full behavior, the data contract, fail-closed handling, the
risk-flag rules, and the publish gate.