After evaluating 93 items across 12 low-code AI agent development tools we found out the 3 most important selection criteria:
1. Two main product categories emerge - AI Native and AI Pivoting
2. The AI market moves too fast for enterprise adoption
3. Deterministic and code-based logic can bring an AI agent to an enterprise standard
In one of the most comprehensive technical market reports on AI Agents so far, independent research analyst Andrew Green conducted a transparent evaluation of workflow-based automation tools for writing AI agents.
AI Native vs AI Pivoting
AI Native tools are mostly startups that have built their platform extensively (and exclusively) to build AI agents
- They offer a great deal of control over agents’ behavior
- Have a limited integrations mechanism and portfolio
AI Pivoting tools are Workflow builders with AI retrofitted
- They are established players with proven enterprise production deployments
- Have a great integration portfolio
- Offer basic features for building agentic systems
Incumbency and Innovation
An integrations portfolio and ecosystem take years to build, so regardless of how quickly a startup can deliver, these are long exercises. This is a clear advantage for incumbent workflow automation tools
New Agent-related capabilities are constantly rolled out, which is disruptive for both established players and their large customers. AI native tools are the likely first-to-market with all new releases
This means that AI Native and AI Pivoting tools are informally locked in to some market segments
- Large enterprise can throttle AI market innovations in favor of stability with AI pivoters
- Startups can grow alongside their AI native providers
Deterministic logic
LLMs are inherently volatile, so for an Agent to conduct their tasks in a predictable manner, you must define deterministic logic to control both their inputs and outputs, especially in consumer-facing applications.
1. Two main product categories emerge - AI Native and AI Pivoting 2. The AI market moves too fast for enterprise adoption 3. Deterministic and code-based logic can bring an AI agent to an enterprise standard
In one of the most comprehensive technical market reports on AI Agents so far, independent research analyst Andrew Green conducted a transparent evaluation of workflow-based automation tools for writing AI agents.
AI Native vs AI Pivoting
AI Native tools are mostly startups that have built their platform extensively (and exclusively) to build AI agents - They offer a great deal of control over agents’ behavior - Have a limited integrations mechanism and portfolio
AI Pivoting tools are Workflow builders with AI retrofitted
- They are established players with proven enterprise production deployments - Have a great integration portfolio - Offer basic features for building agentic systems
Incumbency and Innovation An integrations portfolio and ecosystem take years to build, so regardless of how quickly a startup can deliver, these are long exercises. This is a clear advantage for incumbent workflow automation tools
New Agent-related capabilities are constantly rolled out, which is disruptive for both established players and their large customers. AI native tools are the likely first-to-market with all new releases
This means that AI Native and AI Pivoting tools are informally locked in to some market segments - Large enterprise can throttle AI market innovations in favor of stability with AI pivoters - Startups can grow alongside their AI native providers
Deterministic logic LLMs are inherently volatile, so for an Agent to conduct their tasks in a predictable manner, you must define deterministic logic to control both their inputs and outputs, especially in consumer-facing applications.
Raw results here: https://docs.google.com/spreadsheets/d/1yfSdf4BP1AqBRB0SeaOj...