Galea plugs into the agent workflows teams already run, follows every event, and turns raw traces into company-aware answers: what happened, what mattered, what was risky, and what should improve next.
Agents now refund customers, redline contracts, summarize medical visits, and change product data. When they fail, teams need an answer, not a log dump.
Mercury, LangGraph, OpenAI, Claude, CrewAI, custom queues. The winning observability layer cannot require a rewrite.
Harvey cares about correctness. Decagon cares about refund risk. Cursor cares about unsafe edits. Generic dashboards miss the point.
Agent runtimes can show events. Existing observability tools can show spans. But product teams still have to manually inspect a workflow and decide whether it was correct, allowed, efficient, safe, and worth changing.
The trace says a tool ran. It does not say whether that tool should have run for this customer, user, matter, ticket, or policy.
Latency, cost, correctness, compliance, context size, and risky edits are not equally important. Every company weights them differently.
A workflow can finish successfully while using 2x normal tokens, citing unsupported facts, or editing data it should not touch.
Teams debug one run, then move on. They rarely convert the failure into a reusable eval, baseline, alert, or workflow fix.
Keep Mercury, LangGraph, OpenAI, Claude, CrewAI, Temporal, or custom code. Galea listens to the workflow events, builds the timeline, applies company context, and produces the investigation. From the team's point of view, Galea just works.
Galea is useful because it does not treat every workflow the same. It learns what each company cares about and investigates against that priority model.
Galea's investigator/validator/summarizer walks each workflow with the user. It reads the trace, the company profile, the product surface, prior incidents, and customer priorities, then flags what that team actually cares about: correctness, token usage, context bloat, latency, tool risk, data edits, or anomalous behavior.
Galea looks past success/failure status. It compares each run to the company's baseline, risk model, and product priorities, then explains the part a human should care about.
The run completed, but context grew across three retries and doubled token spend against the customer's normal baseline.
The final answer cited data that was never retrieved. For Harvey, that matters more than latency or cost.
The workflow touched a field that normally requires review. Galea flags the run and recommends a guardrail.
The point is not another dashboard. Galea turns each investigated workflow into a reusable improvement: an alert, eval, baseline, policy suggestion, or product fix.
Galea is not another orchestrator. It is the investigation and optimization layer that works regardless of orchestration layer. Same buyer gravity as Datadog and Sentry, but the unit of analysis is an agent workflow.
The orchestration layer will vary by team. The need to understand, audit, and improve agent behavior will not. Galea becomes the neutral layer that watches every run, explains what mattered, and turns incidents into better workflows.