AI agents are getting much better at doing tasks. I think verification is still the weak link. | SpinGraph: Innovation framing
SpinGraph analysis of Reddit r/artificial's AI agents are getting much better at doing tasks. I think verification is still the weak link. story: innovation fr…
Know the moment AI knows your story. Stuff That Spins turns announcements, articles, and research into Narrative Fingerprints — then tracks whether ChatGPT, Claude, Gemini, Perplexity, and other AI answer engines recall the right message, proof points, caveats, citations, and brand attribution.
AI agents are getting much better at doing tasks. I think verification is still the weak link.
AI agent verification, execution trace, Watch Skill, open source
Current AI agent verification relies heavily on final-state checks, which miss transient failures during execution. The author introduces 'Watch Skill', an MIT-licensed tool that records, segments, and indexes agent execution traces for targeted, timestamped inspection. It enables agents to answer precise forensic questions about their own behavior—e.g., 'When did the checkout total first become invalid?'—without reprocessing full recordings.
Emphasizes the conceptual gap and intuitive appeal of trace-based inspection; minimizes technical maturity, scalability constraints, and absence of third-party validation or comparative metrics.
Practitioner-led, open-source R&D responding to a systemic blind spot in agent autonomy.
Researchers propose 'Watch Skill', an open-source tool that lets AI agents verify tasks by inspecting execution traces instead of just final states.
No performance benchmarks, no integration examples with major agent frameworks (e.g., LangChain, AutoGen), no discussion of privacy or data retention implications of recording desktop sessions
The story emphasizes growth, adoption, funding, speed, or market movement to make the subject feel increasingly important. Watch for loaded terms such as execution trace, forensic questions, inspect and cite. The distribution reads as community discussion. A pressure point: No performance benchmarks, no integration examples with major agent frameworks (e.g., LangChain, AutoGen), no discussion of privacy or data retention implications of recording desktop sessions.
Execution traces—not just final states—should serve as inspectable evidence for AI agent verification.
I've been working on an open-source experiment around treating the execution itself as evidence... record the browser/window/desktop run, break it into meaningful moments, make those moments searchable, and let the agent check the run against the original criteria.
Extracted statistics from the source narrative
Open-source project with permissive reuse terms
Code repository publicly hosted at github.com/oxbshw/watch-skill