Standard AI agents drop the entire task when a single API call fails. That ruins the promise of autonomous analysis.
Today we deployed new error recovery middleware to the Solidmatics AI layer.
When you rely on AI to track engineering work across GitHub, Jira, and Slack, the system constantly interacts with external APIs. External systems reject calls. Arguments occasionally mismatch. When that happens in a standard setup, the agent terminates the process and loses all progress.
Self-correcting tool executions
We upgraded our agent to intercept these tool invocation errors gracefully. 🔧
Now, if a tool throws an error, the middleware catches it. It converts the failure into an error status message containing a hint about what went wrong. The AI model reads this feedback, corrects its own input arguments, and retries the operation.
The agent continues the run instead of aborting. Transport errors from external systems are now treated as normal, recoverable steps rather than fatal exceptions. The system still respects intentional user cancellations, but it handles API hiccups autonomously.
Expanded recursion limits
We also increased the recursion limits for all agent runs. 📈
Evaluating whether software work is real requires deep context. Analyzing a pull request, tracing it to a Jira ticket, and mapping it to a Slack conversation takes multiple consecutive steps.
Very deep sub-agent calls demand high capacity. By raising our recursion limit, the AI now has the headroom to finish complex, multi-step operations without prematurely terminating. The logic runs deeper, which means the daily readouts you receive are more thorough.
Leaders need to know if their engineering spend is producing actual meaning, not just motion. A fragile reporting system cannot provide that confidence.
This update ensures our engine keeps running smoothly behind the scenes. You get the unvarnished reality of your team's progress every morning without ever having to configure a dashboard or restart a failed task. ðŸ§