Detect and throttle runaway LLM agent loops to cut API spend

Many engineering teams embedding multi-step LLM agents now face runaway loops where agent tool calls recurse unexpectedly and burn model spend; SREs, platform engineers and finance owners can see incidents that waste thousands to tens of thousands of dollars in minutes on large-model APIs. These failures are hard to diagnose because calls hop across agents, tools and external APIs, leaving little real-time visibility or automated brakes. You could build a developer-first observability and enforcement platform that traces agent decisions end-to-end, detects loop patterns in real time, and applies automated throttles, per-agent budgets and policy-as-code controls via lightweight SDKs and API integrations with OpenAI/Anthropic/AWS. Include replayable traces, cost-attribution dashboards that map behavior to dollars, and governance hooks for enterprise billing reconciliation and alerting. The timing is favorable: agentization of apps, an observability-first approach to model pipelines, and acute cost sensitivity together drive demand into an addressable market estimated at $4.0B (200,000 developer teams × $20K ACV), and the opportunity scores well (Market Score 85, Revenue Potential 84). Teams are actively looking for tools that tie behavior to spend and can enforce safety automatically. To stand out, focus on agent-specific detection heuristics and causal tracing across tool boundaries, build enforcement that operates with sub-second latency and low false-positive rates, and offer integrations that surface savings directly in finance workflows; competition is medium and fragmented between general APM vendors and nascent AI-observability startups. The biggest challenges will be earning developer trust with non‑intrusive instrumentation and proving ROI in pilots, but targeting high-risk verticals and delivering an early $10K+ avoided-spend case study could drive enterprise traction.