Executive Summary

Engineering, platform, security and compliance teams at mid-to-large enterprises are already struggling with fragmented agent deployments: many organizations are running chains of autonomous agents across 5+ LLM providers and a mix of cloud and on‑prem models, and they lack a consistent way to coordinate, observe and govern those agents. The consequence is brittle workflows, duplicated integration work, and poor auditability when agents take autonomous actions—problems that fall on developer platform teams, SREs and legal/compliance groups to resolve. You could build a two‑layer product: an open protocol that standardizes agent-to-agent messaging, capability discovery and provenance, plus a managed platform that provides runtime orchestration, connectors to major LLM vendors, SDKs, a policy engine for real‑time guardrails, and immutable audit logs. Include enterprise necessities out of the gate—RBAC, on‑prem connectors, encryption, and a simulation/test harness for deterministic replay—so the offering can be sold both as SaaS and as a hybrid appliance at an average ACV target in the $12K neighborhood for scale customers. The timing is favorable: multi‑model deployments and the shift from isolated automations to agentized workflows are creating real demand for an abstraction layer, while regulatory and compliance pressure raises willingness to pay for provenance and explainability. The market math supports this—roughly 1,000,000 target organizations × $12K ACV = $12.0B TAM, with a market score of 92/100 and revenue potential 88/100—yet competition is only medium, leaving room for a standards‑led entrant. To stand out, prioritize vendor neutrality (an open protocol and certified connectors), developer ergonomics, and governance primitives that enterprise buyers require; focus early on integrations with 3–5 leading LLM vendors and a small set of reference customers to build network effects. Be realistic about challenges: achieving protocol adoption is slow, cloud hyperscalers may introduce competing offerings, and you will need deep investment in security, testing and compliance features before enterprise sales scale—but if you can demonstrate reduced integration costs and auditable agent behavior, the payoff is substantial.

Market Opportunity

Protocol and platform to connect and coordinate autonomous AI agents targets a $12.0B = 1,000,000 organizations × $12K ACV total addressable market with medium saturation and a year-over-year growth rate of 30% YoY — enterprise AI platform and infrastructure spend, according to recent industry reports from McKinsey and IDC (2023-2025 projections)..

Key trends driving demand: Multi-model deployments — organizations are adopting multiple LLM providers and on-prem models, creating demand for an abstraction and orchestration layer that spans vendors.; Shift from point automation to agentized workflows — teams are composing chains of autonomous agents to handle end-to-end tasks, which increases the need for coordination and protocol standards.; Regulatory and compliance pressure — companies need audit trails, explainability, and policy enforcement when agents act autonomously, creating a market for governance tooling tied to agent orchestration..

Key competitors include LangChain, OpenAI (Agent & function APIs), AutoGPT / Community Agent Projects.

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Protocol and platform to connect and coordinate autonomous AI agents

Executive Summary

Market Validation

Market Opportunity

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