Tessary
Stop your agents from failing silently in production
Tessary launched on October 22, 2026 with 23 votes, #574 of 3,546 launches that month and more than 95% of that year's launches.
What it does
Tessary is an open-source agent reliability platform. It monitors every production trace, detects failures that sampled evals miss, and investigates the root cause with your agent codebase. It runs cheap, narrow classifiers across every trace. When it detects a recurring issue or change in behaviour, it groups the affected traces into a finding, validates the issue, and investigates what caused it. This keeps monitoring cheap, with deeper analysis reserved for issues that warrant investigation.
Tessary is an open-source agent reliability platform. It monitors every production trace, detects issues that sampled evals miss, and investigates to find the root cause.
Built for engineers running agents in production, Tessary watches every trace, catches what broke, and shows you why. Your agent already emits spans. Tessary reads all of them as they land – no sampling, no LLM call. Twelve traces break the same way. Tessary groups them into one case. We read the failing traces against your repo and find the change that explains it. You get the cause and the evidence behind it, minutes after the first bad trace. Monitor every production trace to understand how your agents behave over time. Check every trace against focused conditions to detect failures, behavioral drift, and users frustrated with your agent. Turn validated issues into cases your team can…from tessary.ai
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KastraJul 2026 · kastra.ai · ▲332Hi HN, Fernando and I built Kastra. Kastra intercepts AI agent tool calls and evaluates them against deterministic policies before they execute. We built this product to control pre- and post-inference workloads. This is aimed at developers using coding agents like Claude Code, Codex, Cursor, and OpenClaw. Kastra pushes an allow, hold, and deny decision before the action runs. You can build these policies in plain English from the web app. The interception engine evaluates the tools, targets, and parameters of every action at sub 1ms at a scale of billions of interceptions per day. We also…
Progress AI ObservabilityAug 2026 · telerik.com · ▲168Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Oodle.ai – $10 per million agent tracesJul 2026 · oodle.ai · ▲31Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Halo – open-source, tamper-evident runtime evidence for AI agentsJul 2026 · github.com · ▲37Hi HN, I'm Brian, I spent the last few years at Vanta (YC W18), helping startups and enterprises become compliant and I recently started exploring what that might look like in a post-agentic world. The problem Halo solves is: when a company buys an AI agent from a vendor and gives it access to their data, they have no way to check what the agent did with that data. Vendors may have built observability dashboards and audit logs, but those are editable and partisan. SOC 2 and ISO 27001 audit a company's controls, but controls are less predictive when the software is agentic. TLDR: give an…
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