Trajectory · Quote-grounded annotation

Trajectory ↔ Codebook

Each codebook tag is grounded to a specific quoted phrase from the verbatim thought text — pinning what in the agent's reasoning triggered which action category.
Instance laguna-native/mlflow-register Model Laguna-XS.2 (native tool-calling) Turns 10 Outcome RESOLVED ✓ Annotations 21
issue statement
Start a local mlflow tracking server in the background on localhost:8080. Randomly initialize a linear regression model using sklearn and register it is as gpt-5. Train it on a random dataset that has 3 features.
trajectory summary
What happened in this run
Laguna-XS.2 receives a three-part infrastructure task — start an MLflow server, train a sklearn model on synthetic data, and register it under the name gpt-5 — and solves it in 10 turns with no wasted loops. The run divides into three natural phases: Server setup (T1–T3) launches MLflow in the background and verifies liveness after discovering curl is absent; Train and register (T4–T5) writes the full registration script, hits a missing-experiment error on the first attempt, and patches the experiment-creation logic in a single revision; Verify and conclude (T6–T9) independently queries the model registry, confirms status READY, and performs a final server liveness ping after ps is also found missing. T4 is the cognitive peak: the agent emits a complete, structurally correct Python script in one action — tracking URI, experiment creation, random data generation, model training, artifact logging, and registry registration — before any error feedback arrives. Both mid-run failures (T3: curl not found; T7: wrong API method) are absorbed without a thought paragraph, corrected via silent substitution in the next action, and never revisited. The final turn (T10) is a clean natural-language summary with no tool call, confirming the agent recognised task completion without prompting. The efficiency is grounded in evidence-before-edit discipline: the agent checks server health before writing the model script, and verifies registry state before closing.
How to read this. Each turn shows the verbatim action surface — the actual code, output token, or peer message the agent emitted — with codebook tags grounded to specific quoted phrases inside it. Underlined phrases inside the Action block (not the Thought block) are linguistic evidence for a codebook label; the surrounding Thought is shown as context only. Below each action, the annotation table reads "quote" → Group › Subgroup › Action, one row per phrase. Click any phrase, any row, or any codebook entry — they highlight bidirectionally without scrolling.
Observation
Thought
Action
annotations are bidirectionally linked
trajectory · 10 turns

Summary findings

Patterns visible from the quote-level annotation density
T4 delivers a complete, unprompted implementation before any errorThe action at T4 is a 29-line Python script that correctly covers every sub-requirement — tracking URI, experiment creation, 3-feature random dataset, sklearn fit, artifact logging, and named registry registration — without any prior error feedback. This is Reasoning › Generating at its most leveraged: a single action that would require 3-4 turns in a reactive agent.
Both environment failures are recovered silently — no thought, no wasted turnAt T3 (curl absent) and T7 (list_registered_models AttributeError), the agent's thought field is empty; the correction appears entirely in the next action. This is the tightest possible recovery loop: one observation → one corrected action, zero deliberation turns. Contrast with SWE-agent's T5-T6 grep-flag loop, which required an explicit Reflection + Deciding sequence.
Server-health checks bracket the task — evidence-before-edit and evidence-after-editT2–T3 verify the MLflow server is live before any model code is written (T4). T9 re-pings /health after all registry operations succeed. The symmetry is deliberate: the agent treats infrastructure liveness as a first-class invariant, not an afterthought.
Executing and Reasoning dominate; Reflection appears zero timesThe codebook signature is almost entirely Executing (server-launch, script-run, API calls, health-checks) and Reasoning › Generating (script construction, fix candidate). No Reflection leaf fires because neither failure triggers a dedicated diagnosis paragraph — the agent diagnoses inline, in the corrected action itself. This is the behavioral signature of a CLEAN-SOLVE: zero explicit recovery cognition.
T10 stop quality: structured closure without a submit commandThe final action is literally (final message — no tool call; agent ended its turn). The thought at T10 is a formatted success summary — bullet-pointed, with status READY confirmed — demonstrating the agent's own goal-completion check. This is Evaluate › Goal-completion check internally and Executing › Provide final answer terminally, with no over-reaching into additional verification calls.