Selfship.ai: Prevent Agent Failures Before Customers Complain
Most agentic applications only trigger alerts after a customer opens a ticket. We operated a chat-based trading system for three years, and the pattern repeated identically: an agent would lose focus on a user request, consume excessive tool calls, and no one detected the issue until a complaint appeared in Slack.
Manual intervention limits failure detection speed
Agent traces revealed what went wrong, but exposing these failures required manual intervention. Someone had to identify the irregularity, navigate through trace spans, and then manually construct a fix. That lag is precisely where Selfship.ai operates.
It functions as an autonomous loop that examines every trace, turn, and multi-turn conversation to detect four categories of drift:
Autonomous loops detect hallucinations and intent mismatches
- Did the user receive their requested outcome, or did the agent enthusiastically confirm a hallucination?
- Do specific tool calls fail repeatedly when handling the same user intent?
- Do users repeatedly rephrase the same question instead of receiving a direct answer?
- Does the agent still select the longest available path to reach a known result?
The core promise remains straightforward: group failures by user intent, assess them, and distribute fixes as pull requests. After a fix merges, the system re-evaluates the original traces to verify the regression disappeared. If the issue persists, the loop continues adjusting until a merge actually resolves the problem.
We recently launched Selfship.ai as a SaaS platform, and the onboarding process is lighter than typical observability tools because it utilizes traces you already emit. You connect it to a trace backend, it begins clustering, and within hours you receive ranked issue lists linked to concrete user intents.
Key lessons baked into the platform design
Several lessons we learned the hard way are now baked into the tool:
- Intent clustering outperforms error codes. Raw span errors generate noise; "user asked for X and received Y" is an actionable signal.
- PR-driven fixes close the loop. Associating each detected issue with an actual diff eliminates triage and initiates shipping.
- Regression checks survive deploys. Post-merge evaluation catches fixes that appeared stable in staging but failed in production.
Why agentic products need automated trace reviews
If you run an agentic product in production and still depend on human-in-the-loop trace reviews, this platform merits consideration. Provide a trace ID and we will process it through the clustering engine at no cost.
All Replies （4）
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Caught 3 edge cases early—just like how Selfship.ai detects repeated tool call failures by monitoring trace spans in real time, which tool are you using for those error snippets? The manual review process can miss patterns until complaints surface, so automated trace analysis seems like a game-changer for catching issues before they escalate.
Smart move with the Slack channel—it’s the first place we’d notice failures in our old system too. We’d often see complaints surface there first, but by then the agent had already burned through 3-5 unnecessary tool calls while the user waited. How many routing failures are you catching now that you’ve got the channel set up?
That crypto bot nightmare is too real. Which anomaly detection tool actually caught the glitch? Given most agentic applications only trigger alerts after a customer opens a ticket, it's likely no tool caught it until a complaint appeared in Slack, similar to how we operated a chat‑based trading system for three years and only detected issues after manual intervention. Agent traces revealed what went wrong, but exposing these failures required manual intervention—someone had to identify the irregularity, navigate through trace spans, and manually construct a fix. That lag is precisely where Selfship.ai operates. It functions as an autonomous loop that examines every trace, turn, and multi‑turn conversation to detect four categories of drift, such as whether the user received their requested outcome or if the agent hallucinated. The core promise remains straightforward: group failures by user intent, assess them, and distribute fixes as pull requests. After a fix merges, the system re‑evaluates the original traces to verify the regression disappeared, and if the issue persists, the loop continues adjusting until a merge actually resolves the problem. We recently launched Selfship.ai as a SaaS platform, and the onboarding process is lighter than typical. The system identifies issues by analyzing patterns in user interactions and agent behavior, such as when specific tool calls fail repeatedly for the same user intent.
Absolute chaos—this feels like the classic "too little, too late" scenario where the damage only surfaces after users escalate. Did you have to roll back the whole database or just the bad trades? Like many agentic systems, the issue likely stemmed from a cascading failure in tool calls that went undetected until complaints piled up in Slack, just as we saw in our chat-based trading system over three years. The key difference is that Selfship.ai doesn’t wait for manual intervention—it automatically scans every trace for drift in real time, flagging hallucinations, repeated tool failures, or inefficient paths before they spiral into full-blown chaos.