Reliable long-running AI agents — orchestration, state & recovery

Many engineering teams building autonomous or semi‑autonomous systems struggle to run long‑lived AI agents reliably: processes drift, memory and context degrade, external APIs change, and recovering a multi‑step agent mid‑flight is ad hoc and manual. This problem is most acute for product, platform and SRE teams across roughly 700,000 engineering orgs that could reasonably spend about $40,000 per year on agent orchestration, MLOps and platform tooling. You could build a managed orchestration platform that provides durable state management, transactional checkpoints, deterministic replay, vector-backed long‑term memory, automated recovery and comprehensive audit trails with RBAC and policy controls. Bundled SDKs and connectors for popular vector DBs and cloud vendors, plus cost/latency controls and a policy engine for safety and compliance, would target enterprise ACV of $30–60K while prioritizing low‑latency state access and compact snapshotting to control storage and inference costs. This market is attractive now because function‑calling LLMs, mature embeddings and cheaper vector storage make persistent agents practical, and enterprise demand for safety, explainability and auditability is growing; the addressable market is roughly $28.0B (market score 92/100, revenue potential 88/100). To stand out versus medium competition you should emphasize operational reliability and compliance as core differentiators, while being frank that integration complexity, cost tradeoffs around storage and inference, and adversarial safety risks are significant engineering and go‑to‑market challenges.