The AI industry has invested $1 trillion to build the most sophisticated reasoning engines in human history. Yet today, we use them as glorified writing assistants — copy-pasting their output into the actual systems where work happens. This isn’t AI’s failure. It’s ours.
We’ve created a bizarre workaround: humans acting as the application layer for AI. A few breakthrough tools have solved this — developers use Github Copilot and Cursor to let AI code directly, for example. But once you step outside the islands of software development, it’s an ocean of copy and paste. Humans today spend an inordinate amount of time ferrying data between LLMs and their actual work tools.
When you’re copying and pasting in and out of ChatGPT, you’ve become the API.
The problem is LLMs are not fully equipped to perform knowledge work. Knowledge work follows a universal pattern: gather information, analyze it, act on the results. LLMs have mastered analysis — they can architect solutions to complex problems with superhuman speed. But they're blind and paralyzed. They can’t see your customer data, can’t access your inventory systems, and can’t execute your business processes.
This creates an absurd situation. We have LLMs that can write perfect SQL queries but can’t connect to your database to run them. We have LLMs that can reason and work faster than any human can, but are held back by the fact that they can’t actually connect to anything.
The result? A $1 trillion investment in thinking machines that require human clerks to do their typing.
The solution isn’t better models — it’s giving LLMs eyes and hands. With the right application layer, LLMs transform from advisors to workers. They don’t just analyze copied-and-pasted data; they directly retrieve it from your systems. They don’t just suggest actions; they execute them.
The insight isn’t about making LLMs smarter. It’s about constraining their execution while preserving their reasoning.
Consider employee onboarding. Let’s say you have a remote employee starting on Monday, but they happen to be in NYC and want to come into the office on Monday. No policy covers there — but the agent infers that the laptop must get there on Friday, checks shipping cutoffs, realizes standard shipping won’t work, and upgrades to overnight delivery. The reasoning is creative — inferring deadlines from travel plans, working around natural disasters. But the execution? The agent calls updateShipping('overnight') or changeDeliveryAddress('NYC’). Exactly as specified.
This separation — creative reasoning, deterministic execution — is what makes AI useful inside companies. You get the LLM’s ability to handle complexity and exceptions, but actions flow through your existing, tested business logic. The agent thinks like a smart employee but acts like well-written code.
This is creative thinking with precise execution.
Today we’re launching Retool Agents — LLMs that work, not just think. An agent isn’t another chatbot. It’s an autonomous worker that completes entire business processes. They make decisions, execute workflows, and use your data to handle complex tasks that used to require human judgement.
While ChatGPT can write refund policies, a Retool Agent actually processes refunds — dynamically pulling customer data from postgres, checking return windows from your internal policy docs, calculating amounts via NetSuite, updating inventory via your ERP system, issuing credits via Stripe, and notifying customers via Twilio. It observes, thinks, and acts in a continuous loop, selecting the right tools for each situation. The agent autonomously manages the entire workflow, end-to-end — no human required.
What truly unlocks AI’s potential isn’t just the models — it’s the integration with your existing tools. Agents inherit every tool your team has built in Retool. Every database query, every workflow becomes a tool in the LLM’s arsenal. Your years of internal tool development instantly become your AI workforce's toolkit — tools they can combine and deploy in ways you never explicitly programmed.
Beyond integrating with all your tools, we provide complete visibility into how Agents operate. We let you watch your Agents work in real-time. We call this view “god view” internally because you can see every decision, every data retrieval, every action. When an Agent processes a refund, you can see it reason through the problem, select the appropriate queries, execute them, evaluate the results, and decide its next action. Complete transparency, full auditability, zero black boxes.
They use your tools, not generic capabilities: Agents don’t need to learn your business — they inherit it. Every custom query, every specialized API, every business function you’ve built becomes a tool they can intelligently deploy.
They’re model-agnostic: Use Claude 4 for code generation, o3 for complex reasoning, Llama for simple tasks. Mix and match models like you'd assign different tasks to different team members based on their strengths.
They’re observable and debuggable: Every agent action is visible, traceable, and modifiable. You can even watch a replay of any agent run — watch their thinking, inspect their tool selection, rewind their decisions. When something goes wrong, you don’t investigate what happened, instead you watch it happen. You can also import failed runs as test cases, fix the logic, and then deploy the changes to your entire workforce instantly.
They’re secure by default: Agents operate within your existing security perimeter, using your SSO, respecting your RBAC, writing to your audit logs. Everything you’ve set up in Retool for your human workers immediately works for your agents, too.
They scale better than humans: Traditional teams get worse with scale because coordination becomes harder, communication fails, and fatigue builds up. Agents get better. Your 10,000th agent makes decisions as crisply as your first. Add a new capability and every instantiation of your agent improves instantly. Find a better process, deploy it universally in seconds. Traditional organizations face exponentially increasing complexity as they grow; agent workforces scale with perfect linear efficiency.
They create perfect audit trails: Human knowledge work is opaque — you see outputs, not the inputs or rationale. With Agents, every decision comes with a complete replay. For the first time in history, complex judgment work becomes debuggable. Management transforms from oversight to optimization. Instead of sampling work and inferring quality, you have perfect visibility into every action, every tool selected, every reasoning step.
We price Agents by the hour, like human workers. Not tokens, not seats, not API calls. Hours.
This isn’t just convenient pricing. It’s a deliberate statement: AI workers are fungible with human workers. When you pay an Agent $2/hour to process refunds versus a human at $25/hour, the ROI calculation is stark and simple. No complex token mathematics, no usage projections — just labor economics everyone understands.
By pricing AI labor in human terms, we’re making the economics of automation transparent. Every business knows their labor costs. Now they can directly calculate their savings. When an Agent processes refunds 50x faster than a human at 1/10th the cost, the math becomes undeniable.
At Retool, we’ve built the application layer for AI. Our 10,000+ customers have created millions of hyper-specific tools — database queries, API calls, workflows, and functions. These building blocks are the operating system of your business. When LLMs can execute these tools directly, they stop being chatbots and become workers.
Companies including AWS and Databricks have already automated over 100 million hours using Retool. That’s roughly $5 billion in labor value achieved by allowing LLMs to actually talk to your systems.
Our next target: to automate 10% of U.S. labor by 2030. That’s approximately 15 million full-time jobs worth of work. Audacious? Yes. Achievable? Based on our growth rate over the last few months, we’re on track to get there by 2030.
Retool is the proven application layer for AI. While others debate AI’s potential, our customers are deploying it at scale.
Agents are available in public beta today. The question isn’t whether AI will transform your business — it’s whether you’ll lead that transformation or be disrupted by it.
Visit retool.com/agents to get started.
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