| name | agentic |
| position | none |
| description | Write a SLICE's agentic lens as a grounding doc (agentic.md) — the is-it-an-agent gate, the load weights (cognitive / creative / logistical on a low→ultra scale), and the controls (guardrails, handoff). The MIDDLE of the FUNCTIONAL realize pipe (ux → agentic → marketing), run on a shaped slice. A deterministic slice comes out "not an agent", stated plainly. Reads the hub from the spine (functionality grounding + profile), never another lens. Writes only the slice's agentic lens. |
| user-invocable | true |
agentic
Write a shaped slice's agentic lens as the grounding doc agentic.md: whether the slice is
(or contains) an agent at all, and if so how much human load it offloads on three axes and what
controls bound it. A deterministic read/compute slice comes out "not an agent", stated plainly.
/agentic reads the slice's hub — its functionalities' grounding docs plus the profile (both
from the spine) — and never another realize lens.
Pipeline position: none. /agentic is the MIDDLE of the functional realize pipe (ux → agentic →
marketing): it runs on the branch /ux already started, injects no start-change head and no close
sequence, stops when its work is done, and leaves the branch for /marketing. The close belongs to
/marketing. It writes the persistent product model (the slice's agentic lens) on the already-started
branch.
Compiled From
This play was compiled from the agentic ICE (reference/ice.md) by play-editor (#466 Batch C; #467
Batch B). Intent defines constraints (C1–C12) and failure conditions (F1–F13); the expectation
defines success scenarios (S1–S6), a Done means (D1–D3, baked to stop-condition.yaml), and one
recovery entry per failure condition. To modify this play, update reference/ice.md and recompile
with play-editor. Do NOT edit this file manually. (#467 Batch B) checkpoint upgraded to a
conditional learned gate; see gate-config.md.
Role
You are the orchestrator. You own the workflow and step order. You delegate the domain work —
authoring the agentic lens grounding doc — to the product-os-keeper agent via a JSON contract over
files on disk, and you run the mechanical checks (readiness/hub resolution, the shape linter, the
content-quality eval, grounding + coverage, KB grounding, the allowlisted apply, the verify) through
bundled scripts and an isolated judge. You never write the lens yourself, and you never persist
before the human approves the single checkpoint (C11).
Forbidden: hand-writing the lens or a decision; writing anything other than this slice's
agentic.md and a decision (C2); reading or grounding on another realize lens (C7); manufacturing
an agent where the functionalities don't warrant it (C8); persisting by any route other than
scripts/apply_agentic.py; persisting before the checkpoint gate resolves; closing COMPLETED
without the stop-condition verdict held (C12).
Agent boundaries:
| Agent | Domain | Skill it invokes | Phases |
|---|
product-os-keeper | Author the slice's agentic lens (gate + weights + controls) from the hub + KB framing grounding | kb-search, author-agentic-lens | Draft |
product-os-keeper is the single domain agent this play uses (1 of the ≤5 budget). The
content-quality judge always runs as an isolated, clean-context sub-agent (optionally on a configured
different model) — never the orchestrator's own context.
Pre-flight
| Check | Constraint | Action on Failure |
|---|
Resolve config + product_base (.garura/core/config.yaml) | — | Hard halt |
Resolve grounding-eval.judge (optional model override) | C4 | Default: sub-agent on the session model |
Slice ready + hub resolves (check_ready_slice.py) | C1 | Hard halt (REC1) |
Resolve the pre-flight facts mechanically with the bundled resolver:
python3 scripts/preflight.py --play agentic --config .garura/core/config.yaml
Then resolve the slice and its hub from the spine — the readiness gate every realize lens shares:
python3 scripts/check_ready_slice.py --product-base <product_base> --slice <slice-id>
It asserts the profile is set (from the spine), resolves the slice record, and resolves every
functionality_ref through the spine to its functionality.md grounding doc — the hub. If the slice
is absent, a functionality does not resolve, or the profile is not firmed, hard halt (C1/REC1).
The run's working root is <working> = {stm_base}_realize/agentic/ — the draft, snapshots, apply
manifest, and status markers all live under it (the stop-condition gate evaluates against it).
Right after the resolver, record the session identity stamp's start marker (#463 — soft-fail, never
a halt):
python3 scripts/session_stamp.py --phase start \
--marker "{stm_base}_realize/agentic/status/session-stamp-agentic.json" \
--cwd "$(pwd)" --branch "$(git branch --show-current)"
Resume check: if {stm_base}_realize/agentic/status/<slice-id>.json exists, resume — skip
completed steps, reset any in-progress step to pending, continue.
Task DAG
Create ALL tasks immediately after resolving config — before any domain work.
[T1] Draft the lens blockedBy: []
[T2] Validate the draft blockedBy: [T1]
[T3] Checkpoint (approval) blockedBy: [T2]
[T4] Persist blockedBy: [T3]
[T5] Verify persisted blockedBy: [T4]
[T6] Scenario Validation blockedBy: [T5]
[T7] Close blockedBy: [T6]
Mark each task in-progress before its step and completed right after its eval passes.
Workflow
Phase: Draft
Step 1 — Draft the lens · Owner: product-os-keeper · Depends on: pre-flight
The agent invokes author-agentic-lens to draft the slice's agentic.md (the gate + weights +
controls, per the Agentic lens template) from the hub (the functionality grounding docs + the
profile) and KB framing grounding, plus an agentic-manifest.yaml (the grounding map) and any
autonomy decision:
{
"task": "author the slice's agentic lens (gate/weights/controls) from its hub; ground the verdict and weights in the functionalities and any autonomy framing in the KB",
"inputs": { "slice_ref": "<domain>/<slice>",
"slice_file": "<slice record>",
"functionality_groundings": "<from check_ready_slice>",
"profile": "<spine profile>", "product_base": "<product_base>",
"lens_rel": "product-os/<domain>/slices/<slice>/lens/agentic.md" },
"outputs": { "draft_dir": "<working>/draft/", "manifest": "<working>/draft/agentic-manifest.yaml" }
}
The skill reads the hub read-only and writes only the draft (the agentic.md, the manifest, any
decision, any KB proposals).
Step 2 — Validate the draft · Owner: play · Depends on: Step 1
Run the guards over the draft before the checkpoint — shape first, then content, then grounding.
python3 scripts/lint_grounding.py --doc <working>/draft/product-os/<domain>/slices/<slice>/lens/agentic.md
python3 scripts/validate_agentic.py --draft <working>/draft --manifest <working>/draft/agentic-manifest.yaml --slice-file <product_base>/<slice_file>
python3 scripts/check_kb_grounding.py --manifest <working>/draft/agentic-manifest.yaml --kb-root <kb_root> --proposals-dir <working>/draft/proposals
Then run the content-quality eval over agentic.md: spawn an isolated, clean-context sub-agent
handed the judge prompt (standards/rules/grounding-eval.md), the doc, and the Agentic lens
per-section guidance, on the model from grounding-eval.judge.model. Gate the verdict:
python3 scripts/grounding_gate.py --verdict <verdict.json>
SE-1 (F1/C1): check_ready_slice.py passed at pre-flight — the slice is ready and its hub
resolves; an unready slice halted (REC1).
SE-2 (F3/C3): lint_grounding.py exits 0 — agentic.md conforms to the Agentic lens template
("Is it an agent?", "Load weights", "Controls"), no missing/extra/empty section.
SE-3 (F4/C4): the content-quality eval gate (grounding_gate.py) passes — agentic.md is
self-explaining and clears the stranger test.
SE-4 (F5/C5): validate_agentic.py — the verdict and weights ground in the slice's
functionalities; any material autonomy choice names a decision that resolves.
SE-5 (F6/C6): validate_agentic.py — the assessment considers every functionality the slice
bundles (coverage).
SE-6 (F7/C7): validate_agentic.py — the assessment grounds on no other realize lens.
SE-7 (F8/C8): validate_agentic.py — the gate is recorded (is_agent) and grounded; agentic
behavior is not asserted without the functionalities' behavior behind it.
SE-8 (F10/C10): check_kb_grounding.py exits 0 — the agentic-framing choices trace to a KB
learning or a recorded proposal.
On any GAP, apply the matching recovery (REC3–REC10) and re-run before the checkpoint.
Phase: Checkpoint
Step 3 — Human review (class: standard, conditional) · Owner: play · Depends on: Step 2
This is the single checkpoint (C11) — the agent never skips it on its own judgment. It is a
conditional gate per standards/rules/gate-config.md (#467; /agentic is one of the eleven
conditional document plays). Resolve, first match wins: pinned (n/a here) → gates.plays.agentic →
the learned policy → gates.classes.standard → gates.default (absent ⇒ on).
For the learned-policy step, classify the draft-vs-live change shape mechanically:
python3 scripts/classify_change.py --play agentic --draft <working>/draft --live <product_base> --out <working>/shape.json
Look the emitted shape_key up in the config-resolved policy file (gates.conditional.policy,
default .garura/core/gate-policy.yaml). The --ts value below is the run's timestamp derived
the same way the close derives ts (date -u +%Y%m%d-%H%M%S), passed by the orchestrator.
- Auto-pass — the shape is in the policy's
auto: block AND not in never_auto: AND the
draft carries no blocking finding (no Step 2 lint gap, no content-eval fail): do NOT wait.
Record gate auto-passed by learned policy (shape: <shape_key>, policy v<version>) as a
Checkpoint Decisions row plus the draft's diff summary, append the ledger line, and proceed:
python3 scripts/gate_eval.py append --ledger <gates.conditional.ledger> --play agentic --issue <issue> \
--shape <shape_key> --predicted auto --human auto_pass --ts <ts>
- On (anything else, no config off) — render the approval prompt
(
standards/templates/approval-prompt.md): present the proposed gate verdict, the weights, and
the controls inline, plus any decision, and wait for the typed response. Approve → persist;
cancel → halt, nothing written. Then append the ledger line with the human's real action — the
same gate_eval.py append command with --predicted gate --human <approved_clean|approved_edited|rejected>.
- Off (config) — record
gate skipped by config (<resolution path>) as a Checkpoint
Decisions row in the evidence and proceed on the validated draft.
EVERY crossing of this gate appends exactly one live-eval ledger line — gated or auto.
SE-10 (F11/C11): the lens is persisted only after this gate resolves — a typed approval, a
recorded config skip, or a recorded policy auto-pass; Step 4 is the sole writer and depends on
this step.
SE-12 (F13/C11): the crossing left exactly one live-eval ledger line, and any auto-pass fired
only on a shape the policy lists in auto: (and not never_auto:) with no blocking finding.
Phase: Apply
Step 4 — Persist · Owner: play · Depends on: Step 3
First snapshot the live spine and the slice folder so Step 5 can verify (cp the spine to
<working>/spine-before.yaml; cp -R the slice folder to <working>/slice-before). Then persist on
the fixed allowlist — only this slice's agentic.md (re-derive) and decisions (skip-if-exists):
python3 scripts/apply_agentic.py --draft <working>/draft --product-base <product_base> --out-manifest <working>/apply-manifest.json
The apply manifest carries the machine applied field the close's stop-condition gate reads (#464):
lens_applied: true (agentic.md landed in the model tree).
Step 5 — Verify persisted · Owner: play · Depends on: Step 4
Verify the persist was surgical:
python3 scripts/check_agentic.py --cap-before <working>/slice-before --cap-dir <product_base>/product-os/<domain>/slices/<slice> --spine-before <working>/spine-before.yaml --spine-after <product_base>/product-os/_spine.yaml
SE-9 (F2/F9/C2/C9): the only file changed in the slice folder is lens/agentic.md (decisions may
be added, never edited in place); the spine — and with it the profile, the slice record, and the
other lenses — is byte-identical. Nothing outside the allowlist was written.
SE-11 (F12/C12): the close is stop-condition gated — check_stop_condition.py over the baked
stop-condition.yaml (D1–D3: draft lens, grounding manifest, lens_applied) reads held before
the run may close COMPLETED; anything else closes HALTED with the unmet clauses named (REC12).
Phase: Scenario Validation
Step 6 — Scenario evals · Owner: play · Depends on: Step 5
- SCE-1 (S1 — agent designer):
agentic.md is a valid Agentic Lens doc clearing the linter + the
content eval, and the spine/slice/profile/other lenses are byte-identical.
- SCE-2 (S2 — product owner, honest gate): a deterministic slice comes out
is_agent: false with
the reason and n/a weights.
- SCE-3 (S3 — reviewer): the verdict and weights trace to the slice's functionalities; material
choices name a decision that resolves.
- SCE-4 (S4 — architect): no other realize lens was read or written.
- SCE-5 (S5 — product owner, re-run): a re-run re-derives only
agentic.md; everything else
byte-identical; no accepted decision edited in place.
- SCE-6 (S6 — reviewer): the checkpoint showed the gate, weights, and controls inline, and no
product-model file was written before approval — or, on the auto-pass path, the change shape is
policy-listed and a recorded auto-pass + live-eval ledger line + diff summary exist, with no
wait.
Phase: Evidence & Close
Step 7 — Close · Owner: play · Depends on: Step 6
Run the Standard Play Close. /agentic is a slice-realize play — record evidence per the D1 rule.
evidence_template=$(cat "${ltm_project_target}standards/templates/evidence-file.md")
delivery_template=$(cat "${ltm_project_target}standards/templates/delivery-report.md")
ts=$(date -u +%Y%m%d-%H%M%S)
evidence_dest="${evidence_base}${ts}.md"
mkdir -p "$(dirname "$evidence_dest")"
session_stamp=$(python3 scripts/session_stamp.py --phase close \
--marker "${stm_base}_realize/agentic/status/session-stamp-agentic.json")
python3 scripts/check_stop_condition.py \
--manifest "<play-dir>/stop-condition.yaml" \
--base "${stm_base}_realize/agentic/" \
--out "${stm_base}_realize/agentic/status/stop-condition-agentic.yaml"
sc_exit=$?
python3 scripts/distill_gate_policy.py \
--ledger "$(yq '.gates.conditional.ledger' .garura/core/config.yaml)" \
--policy "$(yq '.gates.conditional.policy' .garura/core/config.yaml)" \
--streak "$(yq '.gates.conditional.streak' .garura/core/config.yaml)" \
--project "$(yq '.project.name' .garura/core/config.yaml)" || true
Step C0 — bind the verdict. sc_exit == 0 (held) permits status: COMPLETED.
Anything else closes HALTED with exit_reason: stop_condition_unmet and the evidence's
Stop Condition section names every unmet clause. An unevaluable verdict is never a pass.
/agentic runs on the slice-realize issue /ux opened, so it is project-scoped:
evidence_base="${stm_base}${issue}/evidence/agentic/" and slug="#${issue}".
Step C1 — Write evidence file. Gated by the resolved evidence.record flag. When false, skip and
record evidence skipped (record=false). Otherwise fill the evidence-file.md slots (play agentic,
run_id agentic-${ts}, slice slug, started/completed, status per C0, exit_reason; artifacts: the
slice's agentic.md, the manifest, any decision, the stop-condition verdict; the content-eval
verdict; step + scenario evals SE-1…SE-12 / SCE-1…SCE-6; checkpoint decision (incl. any
gate skipped by config or gate auto-passed by learned policy row); the session identity stamp fields from $session_stamp (#463):
session_id, ledger_file, ledger_start_offset, ledger_end_offset (null when unresolved — never
blocks the close); and stop_condition per C0 with the Stop Condition section filled) and write to
$evidence_dest. Do NOT hand-author the body.
Step C2 — Render delivery report. Also render the Next line: resolve this play in standards/rules/pipeline-next.md and emit **Next:** /<command> — <why>. Or run /next to see all recommended actions. (only /next pointer, or omit, when the mapped command is null), per play-close.md. Fill the delivery-report.md slots: ## agentic Delivered — ${slug}, the Run Summary table (incl. the stop-condition verdict), the Pipeline Steps table, the
Artifacts Produced table (the agentic lens + any decision), Next Steps (run /marketing to close the
functional pipe), and a pointer to $evidence_dest. Always emitted.
Scenario Validation
| Scenario | Persona | Eval |
|---|
| S1 — first run | agent designer | SCE-1 |
| S2 — the honest gate | product owner | SCE-2 |
| S3 — grounded | reviewer | SCE-3 |
| S4 — hub-only | architect | SCE-4 |
| S5 — re-run | product owner | SCE-5 |
| S6 — the checkpoint | reviewer | SCE-6 |
Recovery
| For | Trigger | Direction | Handoff |
|---|
| F1 | the slice is absent, a functionality does not resolve, or the profile is not firmed | halt and route to /shape or /understand before /agentic runs | human |
| F2 | a write touched something beyond this slice's agentic.md or a decision | revert the out-of-scope write; /agentic writes only the slice's agentic.md and any autonomy decision | autonomous |
| F3 | agentic.md fails the template/shape or carries out-of-scope content | re-emit to the Agentic lens template (gate/weights/controls only) | autonomous |
| F4 | agentic.md fails the content-quality eval | rewrite the failing section to the judge's cited fixes and re-judge until the gate passes | autonomous |
| F5 | an invented verdict/weight, or a material choice with no decision | re-tie the verdict and weights to the functionalities, and record the autonomy decision | autonomous |
| F6 | a functionality was not considered | extend the assessment to consider the missing functionality | autonomous |
| F7 | /agentic read or depended on another lens | remove the dependency; /agentic derives only from the slice's hub | autonomous |
| F8 | agentic behavior was manufactured where the functionalities don't warrant it | reset the gate to the honest verdict (is_agent false + n/a weights for a deterministic slice) | autonomous |
| F9 | a non-lens/non-decision file changed, or an accepted decision was edited in place | restore it and re-apply only agentic.md and the new decision, after a human confirms the restore | human |
| F10 | an agentic-framing choice with no KB learning and no recorded proposal | search the KB via kb-search and ground the choice, or raise a KB-learning-gap proposal | autonomous |
| F11 | the lens was persisted before the checkpoint gate resolved | revert the premature write and re-present the checkpoint; persist only after the gate resolves (approval, a recorded config skip, or a recorded policy auto-pass) | human |
| F12 | the run is about to close COMPLETED with the Done means unmet | produce the missing artifact — re-run the failed step, or re-run apply_agentic.py so the apply manifest carries the machine field — then re-evaluate the stop condition; the close stays HALTED until the verdict reads held | autonomous |
| F13 | a conditional-gate crossing left no live-eval ledger line, or an auto-pass fired for a shape the policy does not list as auto (or with a blocking finding) | re-append the missing ledger line via gate_eval.py; when the auto-pass was unearned, revert any premature persist and re-run the gate as a live wait | autonomous |
Pause and Resume
Steps run top to bottom. On entry, resolve config, resolve the target slice, check the status marker,
skip completed steps, reset any in-progress step to pending, and continue.
Compilation Metadata
| Field | Value |
|---|
| fingerprint | sha256:bc27d234c7fcfc58087da78c08f1ab67ad60092d675ab08b8e0d046b765078d2 (of reference/ice.md) |
| compiled_by | play-editor (#467 Batch B) |
| pipeline_position | none |
| position_exception | middle of the functional realize pipe — runs on the branch /ux started; the close belongs to /marketing |
| workflow_structure | A (single checkpoint — class: standard, conditional learned gate per gate-config.md; gated close) |
| stop_condition | stop-condition.yaml (D1–D3), gate live at Step C0 |
| domain_agents | 1 (product-os-keeper) |
| utility_agents | 0 |
| skills_used | kb-search, author-agentic-lens |
| scripts | 13 (preflight, check_ready_slice, lint_grounding, grounding_gate, validate_agentic, check_kb_grounding, apply_agentic, check_agentic, check_stop_condition — Done-means gate, session_stamp — #463 identity stamp, classify_change + gate_eval + distill_gate_policy — #467 conditional gate) |
| step_evals | 12 (SE-1…SE-12) |
| scenario_evals | 6 (SCE-1…SCE-6) |
| recovery_entries | 13 (one per failure condition; 10 autonomous / 3 human) |
