Onboarding agent for new hires

The scenario

A new engineer has thirty questions in week one and will ask maybe six of them. The other twenty-four are the ones that feel too basic to interrupt someone over — what does this acronym mean, who owns this service, why is there a second staging environment. Those are exactly the questions a document set can answer, and exactly the ones nobody searches for because they do not know the vocabulary yet.

Step 1 — Load what already exists

for path in ["onboarding.md", "architecture.md", "oncall-runbook.pdf", "org-chart.csv"]:
    client.upload_file(space_id="handbook", file=path, tags=["internal"])

No curation pass. The point of extraction is that you do not have to pre-chunk or pre-summarise; upload what you have.

Step 2 — Answer in the new hire’s words, not yours

print(client.reason(
    space_id="handbook",
    query="Who do I ask about deploys, and is there anything I should not do in my first week?",
))

reason runs a multi-step loop rather than a single search, which is what lets a vague question find a specific answer.

Step 3 — Let it learn what was missing

When the agent cannot answer, that is a gap in your documentation, and it is worth capturing.

client.record(
    space_id="handbook",
    content="New hires keep asking which environment is safe to test migrations in; it is not documented.",
    metadata={"kind": "doc-gap"},
)

After a month, ask it what people keep asking about. That list is your documentation backlog, written by the people who needed it.

Evals

Take ten real questions from your last new starter’s Slack history.
Ask all ten. Score how many are answered correctly from the docs alone.
For each failure, check the receipt: was the answer not retrieved, or retrieved and cut? Those have different fixes — see the receipt recipe.
Re-run after adding the missing document. The score is the thing to track.

Guardrails

An internal handbook contains things not everyone should read — salary bands, security procedures, unreleased plans. A space is readable by everyone it is shared with, so put restricted material in a different space rather than relying on the question not being asked.

Stale docs answer confidently. Memory does not know your runbook is two reorganisations old. Date what you upload, and prefer a correction with a reason to a silent re-upload.
Do not point it at a wiki dump and walk away. A large corpus of contradictory drafts produces confident contradictions.
Uploads are asynchronous. The first question asked thirty seconds after an upload may legitimately find nothing.

Built from

Both are complete, runnable scripts: document_qa.py, memory_basics.py.

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Onboarding agent for new hires

The scenario

Step 1 — Load what already exists

Step 2 — Answer in the new hire’s words, not yours

Step 3 — Let it learn what was missing

Evals

Guardrails

Built from

​The scenario

​Step 1 — Load what already exists

​Step 2 — Answer in the new hire’s words, not yours

​Step 3 — Let it learn what was missing

​Evals

​Guardrails

​Built from

The scenario

Step 1 — Load what already exists

Step 2 — Answer in the new hire’s words, not yours

Step 3 — Let it learn what was missing

Evals

Guardrails

Built from