24 Signals

Real-Time AI Agent Observability Platform

Monitor, verify, and debug your production AI agents with live progress streams, outcome verification, and context window alerts.

Added May 12, 2026

Developer Tools

AI Infrastructure

Observability

Opportunity Score

Opportunity: Medium (59%)

Evidence Strength

Vol: 6%

Urg: 55%

Spec: 55%

Market Analysis

low

$ high

The Problem

Developers running AI agents in production struggle with silent failures where agents log success but the actual outcome never happens, context windows bloat causing quiet output degradation, and on-call experiences that don't fit traditional sysadmin patterns. Existing logging and monitoring tools weren't built for the non-deterministic nature of LLM-based agents.

Potential Solution

A unified observability platform that surfaces live agent activity, progress, blockers, and decisions in real time across all running agents. It adds a verification layer that confirms actual outcomes occurred (not just tool-call success), tracks context window usage with proactive alerts before degradation, and provides on-call alerting tuned for agent-specific failure modes.

Why Now?

Production deployments of Claude Code, Codex, and custom agents have exploded in 2025-2026, but tooling for observing and verifying their behavior is years behind traditional APM. Teams running 50M+ tokens/month need agent-native monitoring before the next outage.

Showing 1-20 of 24 signals

Job ads

Aug 24, 2026

floqast

Senior DevOps Engineer, AI Platform

Observability for AI workloads. Extend our Grafana platform with the signals AI systems need: token consumption, per-model and per-region latency distributions, throttle and retry rates, tool-call failure taxonomy, sandbox session outcomes, generation success rate, and end-to-end agent traces. Define SLOs against critical user journeys, because an AI SLO that only measures HTTP health measures nothing.

Aug 21, 2026

r/SaaS

When someone asks where your agents sent data, can you answer?

Monitoring and observability are the backbone of any model or agentic system you build. Recent high-profile incidents across the major AI labs kind of proved how much it matters to actually see what your models are doing. **The same logic applies to agents: without observability, tool calls and MCP calls happen in the dark**, and you have no way to triage issues or catch the unexpected ones until something breaks. We built feature around that blind spot.

Podcasts

Aug 5, 2026

The Enterprise AI Pilot-to-Production Playbook 2026 – By The Agentics

Everything AI

It's actually terrifyingly easy. An agent can return a response to a customer with absolute zero latency. It can trigger zero standard error codes. The legacy IT dashboard will be glowing green. Sounds perfect. Right. And yet that same agent might have completely hallucinated a factual policy, chosen the wrong internal software tool to pull customer data from, or quietly corrupted a downstream database record while trying to be helpful. Oh, wow. So it's cheerfully and efficiently doing the wrong thing. Exactly. If you try to run an AI agent on a legacy monitoring stack, you literally cannot see what the agent actually did under the hood. You have to instrument specialized observability from day one.

Job ads

Jul 30, 2026

binance

Binance Accelerator Program - Backend Engineer (AI Pro / Agent Infrastructure)

Investigate and improve Agent quality , including failure analysis, regression detection, prompt / workflow optimization, and guardrail effectiveness. Improve system observability and reliability through tracing, metrics, logging, dashboards, and production monitoring.

Jul 29, 2026

r/AI_Agents

Why does an agent that nails every test case still go sideways after a few hundred real conversations?

If you've shipped an agent built with LangChain, CrewAI, or your own custom loop, you've probably seen this: it nails every test case, then a few weeks into production it starts calling the wrong tool, or quietly leaking something it shouldn't, and nobody notices until a user complains. That gap between passing evals and actually holding up in prod is exactly what got us building Prefactor, and we're live on Product Hunt today, currently sitting at #1. Just search Prefactor. Here's the problem we're solving: Getting an AI agent to work in a demo is easy. But getting it into production and actually knowing it's still doing its job is the hard part. Agents drift over time, leak data they shouldn't, or quietly stop doing what they were built for, and most teams only find out after something's already gone wrong. Dashboards and alerts only tell you what happened after the fact. Prefactor evaluates every run in real time for quality, drift and risk, flags the moment something looks off, and lets you hold, approve or block a run live instead of just logging it. A few specifics for anyone curious: \- Traces 100% of runs (every call, tool and decision), not a sample \- 17 categories of sensitive data / PII detection at runtime \- Human-in-the-loop enforcement via SDK/API so you can pause risky actions \- Around 5 minutes from install to your first traced run Happy to answer anything technical in the comments. If you want to check us out or throw us some support, we're live on Product Hunt today, currently sitting at #1. Just search Prefactor.

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