Signal
Overview
Observability for production agent workflows with traces, cost breakdowns, quality scores, alerts, replays, evals, and structured logs.
Signal is an observability tool for teams running AI agents in production. According to its official product page, teams add the SDK to capture agent runs, tool calls, LLM requests, traces, structured logs, costs, latency, quality scores, replays, and alerts. It is positioned as monitoring for agent workflows across frameworks such as LangChain, CrewAI, AutoGen, custom scripts, and raw API calls. Signal supports use cases such as finding silent agent failures, detecting runaway token costs, auditing what an agent did during a customer request, replaying past runs, monitoring latency and quality drift, and sending alerts through Slack, PagerDuty, or webhooks. The product is currently offered for early access teams, with public launch stated for Q3 2026 on the official page.
Autonomy level
41/100
Our subjective rating of how independently the product carries out its documented tasks, considering the human setup, input, approval and review it needs. This is not a measured success rate.
Comparisons
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Some of the use cases of Signal:
- Monitoring production agent runs in real time
- Tracing multi-agent workflows and tool calls
- Tracking token costs and identifying expensive runs
- Replaying failed or unusual agent executions for debugging
- Setting alerts for latency spikes, quality drops, and cost jumps
- Running built-in evaluations such as hallucination checks, format validation, and safety scoring
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Popularity level: 42%
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