Executive Summary

Knowledge workers and small developer teams spend hours stitching together multi-step workflows—manual handoffs, fragile scripts, and disconnected APIs make routine processes slow, error-prone, and hard to scale. Across an estimated 15 million knowledge teams, the average annual tooling and automation spend is about $1,200, yet many lack platforms to rapidly build, share, and govern reusable agents. Build a platform that lets teams visually compose, test, and deploy LLM-powered agents with built-in connectors, structured-output schemas, and one-click sharing and versioning. Combine low-code templates for business users with a developer SDK and governance controls (audit logs, access policies, and resource limits) so teams can ship reliable, auditable automations quickly. The addressable market is roughly $18.0B (15M teams × $1,200 ACV) and is primed because modern LLMs now support tool invocation and structured outputs, making multi-step agents practical and repeatable. Developer ecosystems like LangChain and vector DBs have commoditized primitives, and buyers are shifting from isolated pilots to platformized automation—factors that support a high market score and strong revenue potential. You can differentiate by focusing on end-to-end orchestration and UX—fast templates, a robust runtime, enterprise connectors, and built-in ROI tracking—rather than re-solving infra primitives other vendors already provide. The key challenges are ensuring agent reliability, integrating heterogeneous stacks, and winning enterprise trust/governance, but those are addressable with disciplined engineering, clear SLAs, and targeted go-to-market motion.

Market Opportunity

Rapidly create and share AI agents to automate workflows targets a $18.0B = 15M knowledge teams × $1,200 ACV (annual tooling & automation spend per team) total addressable market with medium saturation and a year-over-year growth rate of 45% YoY growth in enterprise AI software adoption (source: IDC & McKinsey synthesis of AI software/automation demand, 2024-2026 forecasts).

Key trends driving demand: Trend — LLMs now support tool invocation and structured outputs, making multi-step agents practical and reliable for workflows.; Trend — Developer ecosystems (LangChain, vector DBs) have commoditized lower-level building blocks, increasing demand for higher-level orchestration and UX.; Trend — Companies are shifting spend from isolated point AI pilots to platformized automation that can scale across teams and processes.; Trend — A maker and template economy is emerging where shareable automations and agents can go viral and reduce customer acquisition costs..

Key competitors include OpenAI (Agents / Plugins), LangChain (and ecosystem), AgentGPT (consumer agent builders).

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Rapidly create and share AI agents to automate workflows

Executive Summary

Market Validation

Market Opportunity

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