The agentic design environment in Thunk.AI intelligently assists, interprets, suggests, and steers the rapid creation of thunks (AI agentic workflow applications) guided by the user’s intent.
There are four important dimensions to the design environment:
Natural language (no-code) with intelligent assistance
Pro-active intelligence
Self-service platform
Plan-test-iterate
They contribute to rapid development by a broad range of users across an enterprise organization, and rapid convergence to automated applications that run reliably.
The Thunk.AI workflow application model does not require software engineering skills. It does not need the user to write code. Instead, it needs the user to possess domain knowledge of the process to automate and the intent to automate it. This inherent “no-code” requirement is critical in activating AI automation across the breadth of an enterprise.
In order to make this happen, the development environment is intelligent and assistive, helping the user translate their intent and express it effectively in the application model.
The assistive “planning” AI agent has to operate at different levels of abstraction at different stages of the design process. At the very initial stage, it has to help the user translate a very high-level description of the workflow process (eg: “approve expense receipts”) into a multi-step workflow process with AI instructions for every step. At later stages, it has to help the user refine and tune the AI instructions for each granular artifact within the application.
Most AI authoring environments (or vibe-coding environments) take direction from users, interpret that direction, and generate artifacts based on it. To this extent, the Thunk.AI design environment is similar. However, it goes beyond purely reactive intelligence by also proactively observing opportunities for improvement, suggesting them, and in some cases, automatically making them.
The most visible aspect of pro-active design intelligence is in the AI Suggestions associated with the AI instructions. The AI design agent proposes ways to improve and clarify instruction prompts, and ways to tighten controls by eliminating tools. The design agent can also react to user feedback (thumbs up/thumbs down feedback) during testing by diagnosing causes and suggestions improvements in the way the application is defined.
The Thunk.AI platform makes application development a self-service activity accessible to a non-technical user. Not only are the development tasks (workflow creation, testing, and tuning) self-service, but the deployment and execution of workflow automation is also entirely self-service and automated. There are no containers and configurations to construct, observability pipelines to build, networks to configure, or patches to apply.
This is extremely important because the development of an AI agentic application is very deeply intertwined with executing it to test it. Any friction, delay, or difficulty associated with self-service execution significantly lowers the speed and scale of AI transformation possible.
The Thunk.AI design environment is built around the understanding that there is a plan-test iterative cycle that is needed. AI agentic applications are rarely completely specifiable upfront. Instead, it is almost always the case that they start simple, start to be tested, and gradually, there are two kinds of changes that happen:
The workflow itself gets modified and expanded as more requirements come to light
The details of the AI instructions get refined as testing reveals flaws and gaps
It is therefore extremely valuable that the Thunk.AI design environment includes test data generation, interactive testing, and iterative tuning based on test results. All of these functions are driven by the AI design agent working in collaboration with the user.
The intelligent agentic design environment would not be effective without the underlying constructs of the Thunk.AI application model. But equally, the application model would be complex to use without the design environment that radically amplifies the breadth of access (to every process owner in an enterprise) and radically accelerates the interval from initial idea to production-ready automation.