26 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

Medium opportunity (72%)

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.

Market validation

Search demand

Trend snapshot pending

Competition (0)

No matched competitors yet

Showing 1-20 of 26 signals

Job adsSep 3, 2026

gmp-recruitment-services-s-pte-ltd-199307527d

Machine Learning / AI Engineer

Monitor, debug, and optimize AI systems in production using logging, metrics, tracing, alerting, and production signals to improve latency, throughput, cost, reliability, and safety.

Job adsAug 26, 2026

datadog

Senior Product Solutions Architect - AI Agent

You can translate the industry patterns into the emerging AI Agent observability domain, including agentic workflows, LLM spans, experiments, evaluations, and prompt templates You build deep context across teams and translate it into reusable, scalable solutions

Job adsAug 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.

RedditAug 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.

PodcastsAug 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.

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