Executive Summary

Enterprises that are deploying multi-step LLM agents for automation—particularly regulated companies and large security teams—are discovering a new runtime attack surface: model outputs can be malicious, incorrect, or noncompliant after orchestration, and existing API-level controls and SIEM integrations do not provide low-latency, auditable judgments or provenance for each agent decision. With hundreds of thousands of organizations piloting agent workflows, teams responsible for security, compliance, and ML ops need inline controls that can block, repair, or explain outputs in real time without breaking agent flow. You could build an LLM-judge HTTP proxy that sits between agents and model endpoints and runs lightweight, auditable judgment pipelines: local or hybrid inference of open models for policy checks, an ensemble judge for higher-assurance decisions, cryptographic tamper-evident logging of verdicts, and webhook/human-in-the-loop escalation paths for ambiguous cases. The product would expose standard HTTP hooks so it can drop into existing agent runtimes, provide per-request explainability artifacts, and be sized for enterprise scale with quantized models and optional cloud-offload to balance latency and cost. This is a timely market: we estimate an $18.0B addressable market (600,000 enterprises × $30K ACV) driven by three converging trends—agent proliferation increasing runtime risk, cheaper open-source inference enabling in-proxy judgment at sensible cost, and rising regulatory pressure for explainability and tamper-evident audit trails. Market signals (market score 92/100, revenue potential 88/100) suggest strong buyer demand, especially among finance, healthcare, and large tech organizations with high compliance needs. The idea can stand out by offering low-latency, in-path judgment with verifiable provenance and developer-friendly integrations rather than after-the-fact auditing; using on-prem quantized models plus cloud fallbacks provides an economical, auditable alternative to incumbent cloud-native governance tools. Challenges are concrete: engineering for throughput and latency without excessive false positives, defending the proxy itself from adversarial inputs, and winning enterprise procurement against medium competition from cloud vendors and governance startups—but if those are addressed, the product maps cleanly to an identifiable $30K ACV buyer segment.

Market Opportunity

Secure LLM agents in production via LLM-judge HTTP proxy targets a $18.0B = 600,000 enterprises x $30K ACV (enterprise AI/ML runtime security & governance tooling) total addressable market with medium saturation and a year-over-year growth rate of 35% estimated annual growth for AI security & model governance tooling.

Key trends driving demand: Agent proliferation -- more companies run multi-step LLM agents for automation, increasing attack surface and need for runtime controls.; Open-source inference -- cheaper/customizable LLMs enable affordable in-proxy judgement at scale.; AI regulation & audits -- compliance regimes push enterprises to require explainability and tamper-evident logs.; Infrastructure-as-code for AI -- shift to API-first and proxy patterns makes insertion of judgment layers practical..

Key competitors include CrabTrap (Brex, open-source), Robust Intelligence, Fiddler AI, API Gateways & WAFs (AWS API Gateway, Kong, Apigee).

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Secure LLM agents in production via LLM-judge HTTP proxy

Executive Summary

Market Validation

Market Opportunity

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