Agnost AI
Agnost AI is a product analytics platform for conversational agents that helps teams catch silent failures before users leave. It reads every agent conversation alongside its traces to show where the agent fails, where users get frustrated, what they are asking for, and where churn begins. Thousands of chats are automatically clustered into recurring problems and intents, ranked by impact and linked back to the exact conversations and traces behind each pattern. It detects hallucinations, broken promises, quality issues, policy violations, compliance problems, rising friction, and situations where a technical trace may show success even though the user did not get a useful result. Instead of stopping at dashboards, Agnost identifies the highest-impact fixes and provides supporting evidence, recommended changes, and evals needed to ship improvements safely. It can also generate self-improvement suggestions and open pull requests against system prompts, agent harnesses, etc.
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neatlogs
neatlogs is a collaborative debugging and AI reliability platform that provides all the tools your team needs to identify, understand, and fix AI agent issues efficiently
Here’s what you can do today:
- Trace and replay: See what your agent did, step by step.
- Detect failures: Automatically flag traces using conditions, patterns, and classifiers.
- Investigate: Ask Neat AI to investigate runs and turn its findings into fixes.
- Evals: Route traces to human or AI reviewers and measure quality over time.
- Experiment: Version prompts, test changes, and evaluate them against datasets.
- Fix: Review AI-generated fixes and dispatch them to your coding agent.
- Connect your tools: Connect your apps through Tools and MCPs so Copilot and Neat Agent can use them on your behalf.
- Track production: Monitor cost, latency, errors, tools, and detection trends across your traces.
Unlike other tools that are built only for technical audiences, neatlogs is accessible and understandabl
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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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Atla
Atla is the agent observability and evaluation platform that dives deeper to help you find and fix AI agent failures. It provides real‑time visibility into every thought, tool call, and interaction so you can trace each agent run, understand step‑level errors, and identify root causes of failures. Atla automatically surfaces recurring issues across thousands of traces, stops you from manually combing through logs, and delivers specific, actionable suggestions for improvement based on detected error patterns. You can experiment with models and prompts side by side to compare performance, implement recommended fixes, and measure how changes affect completion rates. Individual traces are summarized into clean, readable narratives for granular inspection, while aggregated patterns give you clarity on systemic problems rather than isolated bugs. Designed to integrate with tools you already use, OpenAI, LangChain, Autogen AI, Pydantic AI, and more.
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