Disconnected AI agents cause friction — unify them with a shared workflow & memory

Executive Summary

Large organizations today deploy dozens of narrow AI agents across functions—customer support, sales ops, dev tools—and those agents rarely share memory or workflow, creating brittle handoffs, duplicated state, and governance gaps. This problem is acute for roughly 800,000 mid and large enterprises that are budgeting for enterprise automation and AI orchestration (~$60,000 ACV each, $48.0B market), and it typically shows up as lost productivity, compliance risk, and high maintenance overhead for ML engineers and SREs. You could build a developer-first orchestration platform that unifies agents with a shared workflow engine and a persistent vector-backed memory layer, plus role-based access, audit trails, retries, and SLA controls; SDKs and low-code builders would make it practical for product teams while connectors ensure integration with existing data sources and LLMs. The core technical promise is deterministic agent handoffs, a single source of truth for context and state, and observability primitives (logs, traces, provenance) tailored for multi-agent flows, with pricing tiers that align to enterprise automation budgets. Market timing favors this approach: LLM commoditization reduces the need for bespoke models, inexpensive vector stores make shared memory practical at scale, and the shift to production AI increases demand for governance and SLAs. To stand out you must own workflow primitives, enterprise security, and integration breadth rather than competing on models; expect medium competition, long sales cycles, and significant integration complexity as primary challenges, and validate value through pilots that demonstrate measurable reductions in operational friction.