Agent flow debugging at scale — lightweight self-hosted observability

Modern engineering teams building autonomous agents and stateful orchestration face a new observability gap: traditional APMs capture low-level metrics and spans but not semantic step-level traces or executable state snapshots needed for step-through debugging across multiple agents. This problem primarily affects platform teams, ML engineers, and SREs at large enterprises—roughly 200,000 potential customers in the addressable market—who often cannot export prompt/state data to third-party clouds because of privacy and compliance constraints. You could build a lightweight, self-hosted "agent flow debugger" with small SDKs to emit semantic traces, an interactive step-through trace viewer, on-demand state snapshots, and connectors to existing APMs, all deployable in private VPCs with targeted sub-2% runtime overhead. The timing is favorable: a $15.0B addressable market (200k enterprises × $75K ACV) with a Market Score of 90/100 and Revenue Potential 82/100 driven by three converging trends—increasing autonomy and statefulness in app logic, strong enterprise demand for data residency, and the rise of open-source agent toolkits that simplify instrumentation. To stand out you must optimize for self-hosting and low overhead, publish interoperable instrumentation libraries or open-source connectors to accelerate integrations, and go-to-market through platform and security buyers rather than general developer channels. Pursue this if you can commit to solving nontrivial instrumentation engineering and a longer enterprise sales motion; the competition is medium and incumbents will add agent-aware features, so your defensibility will hinge on execution speed, privacy-first positioning, and partnership/ecosystem plays.