37 — closed-loop task automation (NL → YAML flow → execute → repair)
Three closed-loop automation patterns against a urirun node, all over the URI contract, all with a pluggable planner (real LLM / offline heuristic / a test stub) so the same loops run live or in CI. NL drives the plan; the node's own results and validation close the loop.
| pattern | loop |
|---|---|
| A. self-repair | NL → plan a flow → execute → on a node error feed that error back to the planner → corrected flow → retry |
| B. goal-verify | plan → execute → probe the node to check the goal is met → if not, re-plan with the observed state → repeat |
| C. agent | observe → planner picks one next action → act → repeat until it says *done* (or a step budget) |
Cross-step data flow uses urirun's <field>_from convention (no ${...} templating): {text_from: "find_py.result.stdout"} feeds an earlier step's output into a later step, resolved by urirun.node.mesh.resolve_step_payload.
Run live (real LLM, real node)
set -a; . ../.env; set +a # LLM_MODEL + OPENROUTER_API_KEY
NODE_URL=http://192.168.188.201:8765 python3 run.pyVerified live on a node ("laptop", 192.168.188.201):
== A. self-repair == ok=True in 1 iteration(s)
== B. goal-verify == ok=True in 1 iteration(s)
== C. agent == ok=True in 2 step(s); reason: OS and top processes captured
session saved: ~/.urirun/laptop/session/closed-loop-<ts>/When the LLM's first plan uses a wrong field, the node answers 'text' is a required property and the self-repair loop feeds that back so the planner fixes it — the loop closes through the node's schema validation (demonstrated separately: message → node error → text → success).
Run offline (CI, no LLM, no remote node)
python3 -m pytest test_closed_loop.py -q # 5 passedThe test spins a local urirun node and drives all three loops with deterministic stub planners — including a forced first-attempt failure that the self-repair loop corrects, and the _from chaining (which python3 → its stdout logged as a note).
Files
closed_loop.py— the three loop functions +execute_flow(with_fromchaining).planners.py—make_llm_planner/make_llm_decider(litellm) andheuristic_planner(offline).run.py— drive all three live against a node; saves a session under~/.urirun/<node>/session/.test_closed_loop.py— offline CI test (local node + stub planners), 5 cases.
Why this is "closed"
A one-shot host ask plans and runs once. These loops add the feedback edge: the node's error (A), the node's observed state (B), or the running transcript (C) flows back into the next decision. Combined with the node's schema validation and urirun.result_degraded (surfaced in the trace), the AI corrects itself instead of emitting a plausible-but-wrong plan and stopping.
See also 32-host-ask-over-relay (one-shot NL→flow over the relay) and 15-llm-yaml-repair (the original repair loop).