Kayba
Kayba makes AI agents self-improve from experience. It learns from an agent’s execution traces to detect failures, fix them, and measure whether the fix actually worked. Instead of relying on generic evals that cannot explain why an agent failed, Kayba derives failure modes from the agent’s own traces and builds custom benchmarks for the user’s domain, so teams can measure improvement against real production failure patterns. Kayba wires tracing into an agent with one line of setup, watches it around the clock, and flags the moment a step stops being recorded. Even good tracing rots as teams ship changes, and steps can quietly stop being captured; Kayba checks the tracing users already have, shows exactly what is broken, points to the file that needs attention, and sends the gap to a coding agent through MCP. The coding agent patches the issue, and Kayba verifies that the trace is actually closed.
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Maxim
Maxim is an agent simulation, evaluation, and observability platform that empowers modern AI teams to deploy agents with quality, reliability, and speed.
Maxim's end-to-end evaluation and data management stack covers every stage of the AI lifecycle, from prompt engineering to pre & post release testing and observability, data-set creation & management, and fine-tuning.
Use Maxim to simulate and test your multi-turn workflows on a wide variety of scenarios and across different user personas before taking your application to production.
Features:
Agent Simulation
Agent Evaluation
Prompt Playground
Logging/Tracing Workflows
Custom Evaluators- AI, Programmatic and Statistical
Dataset Curation
Human-in-the-loop
Use Case:
Simulate and test AI agents
Evals for agentic workflows: pre and post-release
Tracing and debugging multi-agent workflows
Real-time alerts on performance and quality
Creating robust datasets for evals and fine-tuning
Human-in-the-loop workflows
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Scout Monitoring
Scout Monitoring is Application Performance Monitoring (APM) that finds what you can't see in charts.
Scout APM is application performance monitoring that streamlines troubleshooting by helping developers find and fix performance issues before customers ever see them. With real-time alerting, a developer-centric UI, and tracing logic that ties bottlenecks directly to source code, Scout APM helps you spend less time debugging and more time building a great product.
Quickly identify, prioritize, and resolve performance problems – memory bloat, N+1 queries, slow database queries, and more – with an agent that instruments the dependencies you need at a fraction of the overhead.
Scout APM is built for developers, by developers, and monitors Ruby, PHP, Python, Node.js, and Elixir applications.
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Sentry
Sentry is an application monitoring and observability platform that helps developers identify, diagnose, and resolve software issues across web, mobile, backend, and AI-powered applications. Designed with a developer-first approach, the platform combines error monitoring, performance tracing, logs, session replay, profiling, metrics, and uptime monitoring into a unified environment connected by a single trace. Sentry enables engineering teams to quickly move from issue detection to root cause analysis and remediation, providing deep visibility into application behavior and user experiences. Its AI-powered debugging capabilities help developers identify underlying causes, generate fixes, and prevent regressions before code reaches production.
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