Pain: unreliable, unscalable AI agents — Solution: a prescriptive, production-ready harness engineering playbook

Many product and platform engineering teams building agentized applications confront brittle, hard-to-scale AI agents that fail silently, are difficult to test, and lack observability, permissioning, and audit trails. This is not an edge problem — there are roughly 9 million developers and a typical spend of $2,000 per developer per year on AI tooling, training, and runtime integrations, implying an $18.0B addressable market for better production patterns. The proposed offering is a prescriptive, production-ready harness engineering playbook: opinionated SDKs and templates for agent orchestration, memory and tool management patterns, testing and CI/CD harnesses, observability and SLO frameworks, and a policy/permissioning layer with enterprise connectors. Deliverables would combine an open-source reference kit with paid enterprise plugins and a managed compliance/hosting tier so teams can adopt proven patterns and ship agents with measurable reliability and auditability. Timing makes this attractive because the industry is moving from single-call LLMs to persistent agents, vector databases and tool orchestration are mature enough to integrate reliably, and enterprises increasingly require governance—reflected in a Market Score of 92/100 and Revenue Potential of 88/100. To stand out, prioritize vendor-agnostic integrations, SLA-driven templates, and repeatable migration playbooks that demonstrate ROI; be honest that competition is medium and the biggest challenges are maintaining connectors across rapidly changing provider APIs and the engineering cost of enterprise compliance and support.