Stop prompt drift — reproducible AI agent prompt structure for founders

Founders and developer teams building multi-step AI agents increasingly face "prompt drift" where conversational logic, tool calls, and implicit assumptions diverge across environments, causing flaky behavior, missed SLAs, and costly debugging. This is a problem for product and engineering teams at startups and mid-market companies that ship agentized features—there are roughly 9.0M developer teams implied in an $18.0B TAM (at $2,000 ACV) who would pay to avoid repeat outages and confidence gaps in production agents. You could build a reproducible prompt orchestration platform that treats prompts as first-class, versioned contracts: a schema for intents and tool calls, test harnesses and deterministic replay, CI/CD gates, cost/latency observability, and SDKs that inject runtime seeds and assertions into common agent runtimes. The MVP can be a CLI/SDK + CI integrations and a lightweight web console that stores prompt specs, runs contract tests against model providers (OpenAI, Anthropic, etc.), and yields artifactable evidence for releases; target ACVs could range from $50 seat/month for SMBs to $2–10K/year for team or enterprise bundles. This market is attractive now because agentization and tool-augmented LLMs make interactions more brittle, teams are already buying niche dev-tools (many with $50–$10K ACV), and analyst scoring here is high (Market Score 92/100, Revenue Potential 86/100) with medium competition. To stand out you must be developer-first, provide deep integrations with popular orchestration frameworks (LangChain, Semantic Kernel, LlamaIndex), publish an open prompt-spec to avoid lock-in, and focus on reproducibility guarantees and auditability—challenges include shifting provider APIs, the risk of commoditization by large cloud vendors, and the effort required to build the network effects of curated prompt/test libraries.