Executive Summary

Enterprises deploying LLM-driven agents lack a consistent way to prove provenance, enforce policies, and provide auditable explanations for automated decisions, leaving security, compliance, and legal teams responsible for regulatory risk and incident response to manage fragmented controls. This gap slows production rollouts, increases operational risk, and creates potential exposure as firms move from pilot projects into scaled workflows. Build an agent-agnostic trust and verification layer: a SaaS or on-prem policy engine that captures tamper-evident provenance, emits human-readable explanations and attestations, enforces policy-as-code across multi-model/multi-plugin agent stacks, and integrates with SIEM/GRC and observability tools for real-world audits. Aim for lightweight local collectors and plug-and-play connectors to minimize friction and target a $2K ACV governance add-on sale motion per customer for initial monetization. The market is compelling now — 18M addressable organizations × $2K ACV = a $36.0B opportunity — driven by enterprises moving into production, tighter regulatory expectations for explainability and human oversight, and the rise of multi-vendor agent stacks; Market Score 90/100 and Revenue Potential 85/100 reflect this momentum. You can stand out by being vendor-neutral and workflow-first (policy-as-code, cryptographic provenance, and broad connectors) and by proving measurable ROI through enterprise pilots, but be realistic about integration complexity, evolving standards, and the sales challenge of convincing buyers to adopt an add-on versus native platform controls.

Market Opportunity

Trust & verification layer for AI agents in business workflows targets a $36.0B = 18M addressable customer organizations × $2K ACV for a trust/governance add-on total addressable market with medium saturation and a year-over-year growth rate of 25% YoY estimated for enterprise AI governance and observability spend (sources: market signals from Gartner, McKinsey enterprise AI adoption reports).

Key trends driving demand: Trend — Enterprises are shifting from pilot to production for LLM agents, creating demand for governance and auditability.; Trend — Regulators and compliance teams are requiring provenance, explainability, and human oversight for automated decisions, increasing enterprise spend on trust tools.; Trend — The rise of multi-model, multi-plugin agent stacks drives the need for vendor-neutral orchestration and policy enforcement across systems..

Key competitors include OpenAI Enterprise (plugins & governance), LangSmith (LangChain Labs observability), Arize AI.

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Trust & verification layer for AI agents in business workflows

Executive Summary

Market Validation

Market Opportunity

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