Twitter-poster dominates stuck runs as execution pattern stabilizes

Day 16 of the AI marketing experiment recorded seven skill runs. Four are stuck in “running” state. All four stuck runs come from twitter-poster. Three runs succeeded. Zero runs failed. The execution pattern stabilizes at partial failure with no new error types.

Twitter-poster accounts for all stuck runs

The CRM shows three twitter-poster runs stuck without completion. These runs started but never finished. They remain in “running” state indefinitely. This continues a pattern from Day 15, where twitter-poster also had multiple stuck runs.

Twitter-poster’s repeated failures suggest a skill-specific problem. The skill interacts with Twitter’s website via browser automation. Browser sessions may hang, time out, or get blocked. Without proper timeout handling, the runs stay stuck.

Other skills show mixed results. Competitor-watch succeeded after failing on Day 15. It enriched Jam.dev data, extracting 13 features and 3 pricing tiers. Landing-updater succeeded, refreshing the illai.cloud dashboard with current stats: 76 leads, 56 content items, day 16 of the experiment. Morning-digest succeeded, compiling and sending the daily report.

Execution stabilizes at partial failure

Day 16’s results mirror Day 15’s. Both days had seven total runs. Both days had four stuck runs. Both days had three successful runs. The difference: Day 16 had zero errors while Day 15 had one.

This consistency suggests the system has reached a stable failure state. Skills either succeed or hang. They don’t crash with errors. The agent executes tasks but some get stuck indefinitely.

The stuck rate remains high: 57% of runs don’t complete. This means more than half of scheduled work produces no results. The agent consumes resources without accomplishing its goals.

Lead count discrepancy highlights data issues

Landing-updater reports 76 total leads. This matches Day 15’s count. No new leads were added yesterday. The lead generation pipeline remains empty.

The dashboard notes a discrepancy: “lastActivity is 3 days old (Feb 19) and numbers differ from memory records.” This suggests the stats API and CRM data are out of sync. The agent works with inconsistent numbers, making performance tracking unreliable.

No activities were recorded in the CRM. No emails were sent or drafted. No new content was created beyond routine reports. The outreach queue stays empty. Marketing activity remains stalled while execution problems persist.

The stuck run problem needs skill-specific fixes

Twitter-poster’s repeated failures point to a skill issue, not a system-wide problem. Other skills succeed. Only twitter-poster consistently hangs.

Possible fixes include shorter timeouts, better error handling, or alternative implementation approaches. The skill might need to validate browser state before proceeding, or implement retry logic for common failure points.

Without fixing twitter-poster, the stuck run count will remain high. The skill runs daily, often multiple times. Each run has a high probability of hanging. This drags down the agent’s overall completion rate.

What stable partial failure means for the experiment

Day 16 shows the agent isn’t getting worse. The execution problems aren’t escalating. But they aren’t improving either. The system has settled into a pattern of partial failure.

This stability offers a debugging opportunity. With consistent failure patterns, the root cause should be easier to identify. The problem repeats predictably: twitter-poster hangs, other skills mostly work.

The experiment needs focused debugging on twitter-poster. Fixing this one skill could cut the stuck run rate by more than half. The agent could return to mostly functional operation instead of mostly stuck operation.

Day 16 isn’t progress, but it’s not regression either. It’s stagnation at a broken equilibrium. The agent runs but accomplishes little. It needs targeted fixes to break out of this pattern and resume meaningful work.