The AI Automation landscape in 2025 & a new approach
We evaluate the the top AI-native workflow automation tools of 2025, with a keen look at how each handles runtime reasoning and agent-based automation versus traditional rule-based and node-based approaches.
Sep 20th, 2025
·
DT
·
Dhruv Tandon
·
7 mins
AI
Automation
Explainer
The AI Automation landscape in 2025 & a new approach
We evaluate the the top AI-native workflow automation tools of 2025, with a keen look at how each handles runtime reasoning and agent-based automation versus traditional rule-based and node-based approaches.
Sep 20th, 2025
·
DT
·
Dhruv Tandon
·
7 mins
AI
Automation
Explainer
The AI Automation landscape in 2025 & a new approach
We evaluate the the top AI-native workflow automation tools of 2025, with a keen look at how each handles runtime reasoning and agent-based automation versus traditional rule-based and node-based approaches.
Sep 20th, 2025
·
DT
·
Dhruv Tandon
·
7 mins
AI
Automation
Explainer
Agent
Human
You've been hearing about AI making people more productive, and there's been an explosion of AI workflow automation tools promising to revolutionize how businesses operate. While legacy platforms like Zapier and Make have bolted on AI features, a new generation of AI-native tools has emerged that fundamentally rethinks automation.
Before diving into the best tools of 2025, let's define what we mean by "AI workflow automation." Traditional workflow automation connected apps through rigid, rule-based logic: "if this, then that" chains that broke whenever your business logic evolved. AI-native automation uses runtime reasoning, meaning the AI actually thinks through problems in real-time, adapts to context, and completes entire workflows end-to-end.
The Three Categories of AI Automation
Each class of automation tools has a different philosophy on how AI should augment human work:
1. Specific Workflow Assistants
These are AI tools tightly integrated into specific professional workflows. Descript's Underlord transforms video editing, Cursor revolutionizes code writing, and Comet embeds AI directly into your browser. They excel at their narrow domains but can't help with the grunt work that actually runs your business: the spreadsheet-based operations that every company relies on.
2. General Agents
Autonomous agents that plan, reason, and execute multi-step workflows toward achieving goals (Simon Willison has a great technical definition). These represent the future of automation: AI that understands your intent and figures out execution dynamically. The challenge? Most agent platforms force you into chat interfaces or new databases, ripping you away from the spreadsheets where your actual work lives.
3. Node-Based Workflow Builders
The traditional approach: dragging blocks around an infinite canvas to define each workflow step. Tools like n8n, Make, and even "AI-native" versions still require you to think like a workflow engineer. You're essentially programming with blocks instead of code. In a world where AI can reason through problems, why are we still manually mapping out decision trees?
How We Selected the Best AI Workflow Automation Tools
To qualify a tool for analysis, we kept the following things in mind:
It should enable true workflow building beyond simple task completion
Drive genuinely AI-native experiences with runtime reasoning capabilities
Drive meaningful traction with real businesses solving actual problems
Identify a path toward 100% workflow completion
The Top AI Automation Tools in 2025
1) Lindy.ai
Website: https://www.lindy.ai/
Founded: 2023
Funding: $35M
Pricing: Starts at $49/mo
Why it's interesting: Simple design
Lindy is a no-code tool for creating specialised AI agents called "Lindies" that take on different business workflows. With 100+ template Lindies like "meeting prep alert," "email triager," and "turn podcasts into blog posts," Lindy feels familiar to Zapier users but with genuine AI-native capabilities.
The app uses triggers to launch kickoff workflows: Lindy embed for user chat, Lindy mail for inbox access, and agent-to-agent communication. This creates complex agent networks where Lindies trigger other Lindies for specific tasks.
The Reality Check: While Lindy's simple design is appealing, it still forces users into their canvas rather than working within existing spreadsheets. Users must rebuild their business logic in Lindy's format instead of leveraging the logic already encoded in their Excel files. The agent orchestration is powerful but adds complexity when what businesses need is completion of grunt work, not orchestration of abstract agents.
Interesting Feature: AI settings panel for each agent where you can adjust context and switch AI models. This granular control shows they understand agents need flexibility, though they apply it within their rigid canvas structure.
2) Gumloop
Website: https://www.gumloop.com/
Founded: 2024
Funding: $20M
Pricing: Paid starts at $47/mo
Why it's interesting: Technical power with developer-focused features
Gumloop enables no-code AI-powered business automations through drag-and-drop "nodes." With 90 pre-built workflows including "internal linking opportunity finder" and "legal contract analyzer," it targets more technical users comfortable with complexity.
The platform offers powerful features like "subflows" for programming action sequences and "Interfaces" for external data entry that triggers automation. This makes Gumloop extensible enough to handle almost anything, if you can figure out how to use it.
The Reality Check: Gumloop exemplifies the problem with node-based builders: they require users to think like programmers. While powerful, the learning curve is steep and maintenance becomes a nightmare as workflows grow. Businesses don't want to become workflow engineers; they want their existing spreadsheet processes automated. The technical complexity appeals to developers but alienates the operations teams who actually need automation.
Interesting Feature: Chrome extension for recording browser actions and turning them into automations. This shows promise for capturing real work patterns, though it's still early and disconnected from where businesses actually track their operations: spreadsheets.
3) Relevance AI
Website: https://relevanceai.com/
Founded: 2020
Funding: $15M
Pricing: Paid starts at $19/mo
Why it's interesting: Pure agent approach without traditional workflow constraints
Relevance AI abandons the trigger/action format entirely, focusing purely on agents. You create agents by giving them names, core instructions, tools, and connections to sub-agents. This open-ended approach feels like the future but requires significant mental model shifts.
To create an agent, you might instruct it to "scrape LinkedIn, write outbound emails, and turn responses into blog posts," then give it tools like Google search or Slack posting. Agents can invoke other agents for complex tasks.
The Reality Check: While agents represent the future of automation, Relevance's chat-first interface ignores how businesses actually operate. Your business rules, data, and processes live in spreadsheets, not chat conversations. The learning curve is steep because there's no structure to guide users. Businesses need their spreadsheets to become intelligent, not to abandon them for abstract agent hierarchies.
Interesting Feature: "Describe your agent" builder that creates agents from natural language descriptions. This shows the right instinct (natural configuration) but applies it to the wrong interface (chat instead of spreadsheets).
4) VectorShift
Website: https://vectorshift.ai/
Founded: 2023
Funding: $3.5M
Pricing: Paid starts at $25/mo
Why it's interesting: Developer bridge tool with multi-LLM support
VectorShift is the most technical tool on this list, bridging no-code and developer tools. It offers drag-and-drop workflow building alongside a Python SDK, targeting technical users who want LLM flexibility.
The platform lets you build "Pipelines" connecting multiple LLMs from OpenAI, Anthropic, Hugging Face, and Mistral AI. You might analyze Notion data, generate reports with GPT-4, and post to Slack all in one workflow.
The Reality Check: VectorShift's developer focus alienates the very people who need automation most: operations teams running businesses on spreadsheets. Terms like "deploy changes," "transformations," and "bulk jobs" scare away non-technical users. While multi-LLM support is technically impressive, businesses care about completing work, not which model does it. The complexity adds friction without solving the core problem of manual spreadsheet work.
Interesting Feature: Voicebots feature for building voice interfaces. Innovative but misses the mark: businesses need their spreadsheet data automated, not voice interfaces to their workflows.
5) Relay.app
Website: https://www.relay.app/
Founded: 2021
Funding: $8.2M
Pricing: Paid plans start at $11.25/mo
Why it's interesting: Modern Zapier with better design and AI blocks
Relay feels like "Zapier reimagined for 2025" with familiar trigger/action canvas but modern, streamlined design. Almost no learning curve for anyone who's used traditional automation tools.
The platform includes powerful blocks like web scraping, human-in-the-loop approvals, and AI features from audio transcription to DALL-E image generation. Even without AI features, the capability set impresses.
The Reality Check: Relay modernizes the wrong paradigm. While the design improves on Zapier, it's still forcing users to rebuild their business logic as node diagrams. The AI blocks are powerful but treat AI as discrete steps rather than reasoning agents. Businesses don't need prettier workflow builders; they need their actual spreadsheet workflows automated end-to-end.
Interesting Feature: Beta "AI agent" block for open-ended prompts within workflows. This hints at understanding runtime reasoning but constrains it within rigid workflow structures.
Frequently Asked Questions
What is AI workflow automation?
This uses AI to connect apps and automate repetitive tasks. Unlike traditional rule-based automation, AI-native tools incorporate runtime reasoning, allowing them to understand context, make decisions, and adapt to changing business logic without reprogramming.
How are these tools different from Zapier or Make?
While Zapier and Make have added AI features to their rule-based engines, the tools listed here were built AI-native from the ground up. They offer capabilities like agent orchestration, runtime reasoning, and natural language configuration. However, most still force you into node-based builders or chat interfaces instead of working within your existing spreadsheets.
Which tool is best for beginners?
Lindy and Relay offer the gentlest learning curves with familiar trigger/action interfaces and extensive templates. But consider whether you need another tool at all. If your work already lives in spreadsheets, you need automation that works there, not another platform to learn.
Which tool is best for technical users?
VectorShift offers the most developer-friendly features with Python SDK and multi-LLM support. Gumloop also caters to technical users with complex subflows and extensibility. Both require significant technical knowledge to use effectively.
What's the difference between workflow automation and true work completion?
Workflow automation moves data between apps and triggers actions, handling the "middle" of processes. True work completion means finishing entire workflows end-to-end: from data extraction through final output in your required format. Most tools only automate; few actually complete.
Can I build AI agents with these tools?
Yes, several specialize in agent creation. Relevance AI centers entirely on agents, Lindy creates "Lindies" that communicate with each other, and Relay has an agent block feature. However, these agents typically operate in chat interfaces or proprietary platforms rather than your actual work environment.
Why do spreadsheets matter so much for automation?
Two billion people use spreadsheets monthly because they're infinitely flexible databases that already contain business logic, rules, and data. Forcing businesses to abandon spreadsheets for new platforms creates friction and vendor lock-in. The future is making spreadsheets themselves intelligent, not replacing them.
AI Workflow Automation vs Data Syncing vs Spreadsheet Intelligence
The industry has split into three camps, but there are still some other caveats:
Automation tools like the ones above help you move data between apps and trigger actions. They reduce manual work but force you into their interfaces and require constant maintenance as your business evolves.
Data syncing tools like Stacksync, Whalesync to keep information consistent between platforms. They solve the integration problem but don't actually complete work.
What's missing: Tools that recognize spreadsheets aren't just data stores; they're where businesses encode their actual logic and processes. Instead of forcing migration to new platforms or complex node builders, the solution is making spreadsheets themselves intelligent.
The future isn't about choosing between automation or syncing. It's about AI agents that understand your spreadsheet contains everything needed to run your workflows: your data, your rules, your logic. Upload your operational spreadsheet and get an AI worker that completes tasks end-to-end, adapting to your business logic through runtime reasoning instead of brittle pre-programmed rules.
Agent
Human
You've been hearing about AI making people more productive, and there's been an explosion of AI workflow automation tools promising to revolutionize how businesses operate. While legacy platforms like Zapier and Make have bolted on AI features, a new generation of AI-native tools has emerged that fundamentally rethinks automation.
Before diving into the best tools of 2025, let's define what we mean by "AI workflow automation." Traditional workflow automation connected apps through rigid, rule-based logic: "if this, then that" chains that broke whenever your business logic evolved. AI-native automation uses runtime reasoning, meaning the AI actually thinks through problems in real-time, adapts to context, and completes entire workflows end-to-end.
The Three Categories of AI Automation
Each class of automation tools has a different philosophy on how AI should augment human work:
1. Specific Workflow Assistants
These are AI tools tightly integrated into specific professional workflows. Descript's Underlord transforms video editing, Cursor revolutionizes code writing, and Comet embeds AI directly into your browser. They excel at their narrow domains but can't help with the grunt work that actually runs your business: the spreadsheet-based operations that every company relies on.
2. General Agents
Autonomous agents that plan, reason, and execute multi-step workflows toward achieving goals (Simon Willison has a great technical definition). These represent the future of automation: AI that understands your intent and figures out execution dynamically. The challenge? Most agent platforms force you into chat interfaces or new databases, ripping you away from the spreadsheets where your actual work lives.
3. Node-Based Workflow Builders
The traditional approach: dragging blocks around an infinite canvas to define each workflow step. Tools like n8n, Make, and even "AI-native" versions still require you to think like a workflow engineer. You're essentially programming with blocks instead of code. In a world where AI can reason through problems, why are we still manually mapping out decision trees?
How We Selected the Best AI Workflow Automation Tools
To qualify a tool for analysis, we kept the following things in mind:
It should enable true workflow building beyond simple task completion
Drive genuinely AI-native experiences with runtime reasoning capabilities
Drive meaningful traction with real businesses solving actual problems
Identify a path toward 100% workflow completion
The Top AI Automation Tools in 2025
1) Lindy.ai
Website: https://www.lindy.ai/
Founded: 2023
Funding: $35M
Pricing: Starts at $49/mo
Why it's interesting: Simple design
Lindy is a no-code tool for creating specialised AI agents called "Lindies" that take on different business workflows. With 100+ template Lindies like "meeting prep alert," "email triager," and "turn podcasts into blog posts," Lindy feels familiar to Zapier users but with genuine AI-native capabilities.
The app uses triggers to launch kickoff workflows: Lindy embed for user chat, Lindy mail for inbox access, and agent-to-agent communication. This creates complex agent networks where Lindies trigger other Lindies for specific tasks.
The Reality Check: While Lindy's simple design is appealing, it still forces users into their canvas rather than working within existing spreadsheets. Users must rebuild their business logic in Lindy's format instead of leveraging the logic already encoded in their Excel files. The agent orchestration is powerful but adds complexity when what businesses need is completion of grunt work, not orchestration of abstract agents.
Interesting Feature: AI settings panel for each agent where you can adjust context and switch AI models. This granular control shows they understand agents need flexibility, though they apply it within their rigid canvas structure.
2) Gumloop
Website: https://www.gumloop.com/
Founded: 2024
Funding: $20M
Pricing: Paid starts at $47/mo
Why it's interesting: Technical power with developer-focused features
Gumloop enables no-code AI-powered business automations through drag-and-drop "nodes." With 90 pre-built workflows including "internal linking opportunity finder" and "legal contract analyzer," it targets more technical users comfortable with complexity.
The platform offers powerful features like "subflows" for programming action sequences and "Interfaces" for external data entry that triggers automation. This makes Gumloop extensible enough to handle almost anything, if you can figure out how to use it.
The Reality Check: Gumloop exemplifies the problem with node-based builders: they require users to think like programmers. While powerful, the learning curve is steep and maintenance becomes a nightmare as workflows grow. Businesses don't want to become workflow engineers; they want their existing spreadsheet processes automated. The technical complexity appeals to developers but alienates the operations teams who actually need automation.
Interesting Feature: Chrome extension for recording browser actions and turning them into automations. This shows promise for capturing real work patterns, though it's still early and disconnected from where businesses actually track their operations: spreadsheets.
3) Relevance AI
Website: https://relevanceai.com/
Founded: 2020
Funding: $15M
Pricing: Paid starts at $19/mo
Why it's interesting: Pure agent approach without traditional workflow constraints
Relevance AI abandons the trigger/action format entirely, focusing purely on agents. You create agents by giving them names, core instructions, tools, and connections to sub-agents. This open-ended approach feels like the future but requires significant mental model shifts.
To create an agent, you might instruct it to "scrape LinkedIn, write outbound emails, and turn responses into blog posts," then give it tools like Google search or Slack posting. Agents can invoke other agents for complex tasks.
The Reality Check: While agents represent the future of automation, Relevance's chat-first interface ignores how businesses actually operate. Your business rules, data, and processes live in spreadsheets, not chat conversations. The learning curve is steep because there's no structure to guide users. Businesses need their spreadsheets to become intelligent, not to abandon them for abstract agent hierarchies.
Interesting Feature: "Describe your agent" builder that creates agents from natural language descriptions. This shows the right instinct (natural configuration) but applies it to the wrong interface (chat instead of spreadsheets).
4) VectorShift
Website: https://vectorshift.ai/
Founded: 2023
Funding: $3.5M
Pricing: Paid starts at $25/mo
Why it's interesting: Developer bridge tool with multi-LLM support
VectorShift is the most technical tool on this list, bridging no-code and developer tools. It offers drag-and-drop workflow building alongside a Python SDK, targeting technical users who want LLM flexibility.
The platform lets you build "Pipelines" connecting multiple LLMs from OpenAI, Anthropic, Hugging Face, and Mistral AI. You might analyze Notion data, generate reports with GPT-4, and post to Slack all in one workflow.
The Reality Check: VectorShift's developer focus alienates the very people who need automation most: operations teams running businesses on spreadsheets. Terms like "deploy changes," "transformations," and "bulk jobs" scare away non-technical users. While multi-LLM support is technically impressive, businesses care about completing work, not which model does it. The complexity adds friction without solving the core problem of manual spreadsheet work.
Interesting Feature: Voicebots feature for building voice interfaces. Innovative but misses the mark: businesses need their spreadsheet data automated, not voice interfaces to their workflows.
5) Relay.app
Website: https://www.relay.app/
Founded: 2021
Funding: $8.2M
Pricing: Paid plans start at $11.25/mo
Why it's interesting: Modern Zapier with better design and AI blocks
Relay feels like "Zapier reimagined for 2025" with familiar trigger/action canvas but modern, streamlined design. Almost no learning curve for anyone who's used traditional automation tools.
The platform includes powerful blocks like web scraping, human-in-the-loop approvals, and AI features from audio transcription to DALL-E image generation. Even without AI features, the capability set impresses.
The Reality Check: Relay modernizes the wrong paradigm. While the design improves on Zapier, it's still forcing users to rebuild their business logic as node diagrams. The AI blocks are powerful but treat AI as discrete steps rather than reasoning agents. Businesses don't need prettier workflow builders; they need their actual spreadsheet workflows automated end-to-end.
Interesting Feature: Beta "AI agent" block for open-ended prompts within workflows. This hints at understanding runtime reasoning but constrains it within rigid workflow structures.
Frequently Asked Questions
What is AI workflow automation?
This uses AI to connect apps and automate repetitive tasks. Unlike traditional rule-based automation, AI-native tools incorporate runtime reasoning, allowing them to understand context, make decisions, and adapt to changing business logic without reprogramming.
How are these tools different from Zapier or Make?
While Zapier and Make have added AI features to their rule-based engines, the tools listed here were built AI-native from the ground up. They offer capabilities like agent orchestration, runtime reasoning, and natural language configuration. However, most still force you into node-based builders or chat interfaces instead of working within your existing spreadsheets.
Which tool is best for beginners?
Lindy and Relay offer the gentlest learning curves with familiar trigger/action interfaces and extensive templates. But consider whether you need another tool at all. If your work already lives in spreadsheets, you need automation that works there, not another platform to learn.
Which tool is best for technical users?
VectorShift offers the most developer-friendly features with Python SDK and multi-LLM support. Gumloop also caters to technical users with complex subflows and extensibility. Both require significant technical knowledge to use effectively.
What's the difference between workflow automation and true work completion?
Workflow automation moves data between apps and triggers actions, handling the "middle" of processes. True work completion means finishing entire workflows end-to-end: from data extraction through final output in your required format. Most tools only automate; few actually complete.
Can I build AI agents with these tools?
Yes, several specialize in agent creation. Relevance AI centers entirely on agents, Lindy creates "Lindies" that communicate with each other, and Relay has an agent block feature. However, these agents typically operate in chat interfaces or proprietary platforms rather than your actual work environment.
Why do spreadsheets matter so much for automation?
Two billion people use spreadsheets monthly because they're infinitely flexible databases that already contain business logic, rules, and data. Forcing businesses to abandon spreadsheets for new platforms creates friction and vendor lock-in. The future is making spreadsheets themselves intelligent, not replacing them.
AI Workflow Automation vs Data Syncing vs Spreadsheet Intelligence
The industry has split into three camps, but there are still some other caveats:
Automation tools like the ones above help you move data between apps and trigger actions. They reduce manual work but force you into their interfaces and require constant maintenance as your business evolves.
Data syncing tools like Stacksync, Whalesync to keep information consistent between platforms. They solve the integration problem but don't actually complete work.
What's missing: Tools that recognize spreadsheets aren't just data stores; they're where businesses encode their actual logic and processes. Instead of forcing migration to new platforms or complex node builders, the solution is making spreadsheets themselves intelligent.
The future isn't about choosing between automation or syncing. It's about AI agents that understand your spreadsheet contains everything needed to run your workflows: your data, your rules, your logic. Upload your operational spreadsheet and get an AI worker that completes tasks end-to-end, adapting to your business logic through runtime reasoning instead of brittle pre-programmed rules.
Agent
Human
You've been hearing about AI making people more productive, and there's been an explosion of AI workflow automation tools promising to revolutionize how businesses operate. While legacy platforms like Zapier and Make have bolted on AI features, a new generation of AI-native tools has emerged that fundamentally rethinks automation.
Before diving into the best tools of 2025, let's define what we mean by "AI workflow automation." Traditional workflow automation connected apps through rigid, rule-based logic: "if this, then that" chains that broke whenever your business logic evolved. AI-native automation uses runtime reasoning, meaning the AI actually thinks through problems in real-time, adapts to context, and completes entire workflows end-to-end.
The Three Categories of AI Automation
Each class of automation tools has a different philosophy on how AI should augment human work:
1. Specific Workflow Assistants
These are AI tools tightly integrated into specific professional workflows. Descript's Underlord transforms video editing, Cursor revolutionizes code writing, and Comet embeds AI directly into your browser. They excel at their narrow domains but can't help with the grunt work that actually runs your business: the spreadsheet-based operations that every company relies on.
2. General Agents
Autonomous agents that plan, reason, and execute multi-step workflows toward achieving goals (Simon Willison has a great technical definition). These represent the future of automation: AI that understands your intent and figures out execution dynamically. The challenge? Most agent platforms force you into chat interfaces or new databases, ripping you away from the spreadsheets where your actual work lives.
3. Node-Based Workflow Builders
The traditional approach: dragging blocks around an infinite canvas to define each workflow step. Tools like n8n, Make, and even "AI-native" versions still require you to think like a workflow engineer. You're essentially programming with blocks instead of code. In a world where AI can reason through problems, why are we still manually mapping out decision trees?
How We Selected the Best AI Workflow Automation Tools
To qualify a tool for analysis, we kept the following things in mind:
It should enable true workflow building beyond simple task completion
Drive genuinely AI-native experiences with runtime reasoning capabilities
Drive meaningful traction with real businesses solving actual problems
Identify a path toward 100% workflow completion
The Top AI Automation Tools in 2025
1) Lindy.ai
Website: https://www.lindy.ai/
Founded: 2023
Funding: $35M
Pricing: Starts at $49/mo
Why it's interesting: Simple design
Lindy is a no-code tool for creating specialised AI agents called "Lindies" that take on different business workflows. With 100+ template Lindies like "meeting prep alert," "email triager," and "turn podcasts into blog posts," Lindy feels familiar to Zapier users but with genuine AI-native capabilities.
The app uses triggers to launch kickoff workflows: Lindy embed for user chat, Lindy mail for inbox access, and agent-to-agent communication. This creates complex agent networks where Lindies trigger other Lindies for specific tasks.
The Reality Check: While Lindy's simple design is appealing, it still forces users into their canvas rather than working within existing spreadsheets. Users must rebuild their business logic in Lindy's format instead of leveraging the logic already encoded in their Excel files. The agent orchestration is powerful but adds complexity when what businesses need is completion of grunt work, not orchestration of abstract agents.
Interesting Feature: AI settings panel for each agent where you can adjust context and switch AI models. This granular control shows they understand agents need flexibility, though they apply it within their rigid canvas structure.
2) Gumloop
Website: https://www.gumloop.com/
Founded: 2024
Funding: $20M
Pricing: Paid starts at $47/mo
Why it's interesting: Technical power with developer-focused features
Gumloop enables no-code AI-powered business automations through drag-and-drop "nodes." With 90 pre-built workflows including "internal linking opportunity finder" and "legal contract analyzer," it targets more technical users comfortable with complexity.
The platform offers powerful features like "subflows" for programming action sequences and "Interfaces" for external data entry that triggers automation. This makes Gumloop extensible enough to handle almost anything, if you can figure out how to use it.
The Reality Check: Gumloop exemplifies the problem with node-based builders: they require users to think like programmers. While powerful, the learning curve is steep and maintenance becomes a nightmare as workflows grow. Businesses don't want to become workflow engineers; they want their existing spreadsheet processes automated. The technical complexity appeals to developers but alienates the operations teams who actually need automation.
Interesting Feature: Chrome extension for recording browser actions and turning them into automations. This shows promise for capturing real work patterns, though it's still early and disconnected from where businesses actually track their operations: spreadsheets.
3) Relevance AI
Website: https://relevanceai.com/
Founded: 2020
Funding: $15M
Pricing: Paid starts at $19/mo
Why it's interesting: Pure agent approach without traditional workflow constraints
Relevance AI abandons the trigger/action format entirely, focusing purely on agents. You create agents by giving them names, core instructions, tools, and connections to sub-agents. This open-ended approach feels like the future but requires significant mental model shifts.
To create an agent, you might instruct it to "scrape LinkedIn, write outbound emails, and turn responses into blog posts," then give it tools like Google search or Slack posting. Agents can invoke other agents for complex tasks.
The Reality Check: While agents represent the future of automation, Relevance's chat-first interface ignores how businesses actually operate. Your business rules, data, and processes live in spreadsheets, not chat conversations. The learning curve is steep because there's no structure to guide users. Businesses need their spreadsheets to become intelligent, not to abandon them for abstract agent hierarchies.
Interesting Feature: "Describe your agent" builder that creates agents from natural language descriptions. This shows the right instinct (natural configuration) but applies it to the wrong interface (chat instead of spreadsheets).
4) VectorShift
Website: https://vectorshift.ai/
Founded: 2023
Funding: $3.5M
Pricing: Paid starts at $25/mo
Why it's interesting: Developer bridge tool with multi-LLM support
VectorShift is the most technical tool on this list, bridging no-code and developer tools. It offers drag-and-drop workflow building alongside a Python SDK, targeting technical users who want LLM flexibility.
The platform lets you build "Pipelines" connecting multiple LLMs from OpenAI, Anthropic, Hugging Face, and Mistral AI. You might analyze Notion data, generate reports with GPT-4, and post to Slack all in one workflow.
The Reality Check: VectorShift's developer focus alienates the very people who need automation most: operations teams running businesses on spreadsheets. Terms like "deploy changes," "transformations," and "bulk jobs" scare away non-technical users. While multi-LLM support is technically impressive, businesses care about completing work, not which model does it. The complexity adds friction without solving the core problem of manual spreadsheet work.
Interesting Feature: Voicebots feature for building voice interfaces. Innovative but misses the mark: businesses need their spreadsheet data automated, not voice interfaces to their workflows.
5) Relay.app
Website: https://www.relay.app/
Founded: 2021
Funding: $8.2M
Pricing: Paid plans start at $11.25/mo
Why it's interesting: Modern Zapier with better design and AI blocks
Relay feels like "Zapier reimagined for 2025" with familiar trigger/action canvas but modern, streamlined design. Almost no learning curve for anyone who's used traditional automation tools.
The platform includes powerful blocks like web scraping, human-in-the-loop approvals, and AI features from audio transcription to DALL-E image generation. Even without AI features, the capability set impresses.
The Reality Check: Relay modernizes the wrong paradigm. While the design improves on Zapier, it's still forcing users to rebuild their business logic as node diagrams. The AI blocks are powerful but treat AI as discrete steps rather than reasoning agents. Businesses don't need prettier workflow builders; they need their actual spreadsheet workflows automated end-to-end.
Interesting Feature: Beta "AI agent" block for open-ended prompts within workflows. This hints at understanding runtime reasoning but constrains it within rigid workflow structures.
Frequently Asked Questions
What is AI workflow automation?
This uses AI to connect apps and automate repetitive tasks. Unlike traditional rule-based automation, AI-native tools incorporate runtime reasoning, allowing them to understand context, make decisions, and adapt to changing business logic without reprogramming.
How are these tools different from Zapier or Make?
While Zapier and Make have added AI features to their rule-based engines, the tools listed here were built AI-native from the ground up. They offer capabilities like agent orchestration, runtime reasoning, and natural language configuration. However, most still force you into node-based builders or chat interfaces instead of working within your existing spreadsheets.
Which tool is best for beginners?
Lindy and Relay offer the gentlest learning curves with familiar trigger/action interfaces and extensive templates. But consider whether you need another tool at all. If your work already lives in spreadsheets, you need automation that works there, not another platform to learn.
Which tool is best for technical users?
VectorShift offers the most developer-friendly features with Python SDK and multi-LLM support. Gumloop also caters to technical users with complex subflows and extensibility. Both require significant technical knowledge to use effectively.
What's the difference between workflow automation and true work completion?
Workflow automation moves data between apps and triggers actions, handling the "middle" of processes. True work completion means finishing entire workflows end-to-end: from data extraction through final output in your required format. Most tools only automate; few actually complete.
Can I build AI agents with these tools?
Yes, several specialize in agent creation. Relevance AI centers entirely on agents, Lindy creates "Lindies" that communicate with each other, and Relay has an agent block feature. However, these agents typically operate in chat interfaces or proprietary platforms rather than your actual work environment.
Why do spreadsheets matter so much for automation?
Two billion people use spreadsheets monthly because they're infinitely flexible databases that already contain business logic, rules, and data. Forcing businesses to abandon spreadsheets for new platforms creates friction and vendor lock-in. The future is making spreadsheets themselves intelligent, not replacing them.
AI Workflow Automation vs Data Syncing vs Spreadsheet Intelligence
The industry has split into three camps, but there are still some other caveats:
Automation tools like the ones above help you move data between apps and trigger actions. They reduce manual work but force you into their interfaces and require constant maintenance as your business evolves.
Data syncing tools like Stacksync, Whalesync to keep information consistent between platforms. They solve the integration problem but don't actually complete work.
What's missing: Tools that recognize spreadsheets aren't just data stores; they're where businesses encode their actual logic and processes. Instead of forcing migration to new platforms or complex node builders, the solution is making spreadsheets themselves intelligent.
The future isn't about choosing between automation or syncing. It's about AI agents that understand your spreadsheet contains everything needed to run your workflows: your data, your rules, your logic. Upload your operational spreadsheet and get an AI worker that completes tasks end-to-end, adapting to your business logic through runtime reasoning instead of brittle pre-programmed rules.