How to Build Self-Improving Autonomous Agents

6 min readMay 6


Robots working together to assemble a machine

Are you tired of the monotony of the modern work environment? If so, you’re not alone. Fortunately, we’re on the brink of a revolution that will transform the way we work, collaborate, and achieve our goals. In this article, we’ll explore the concept of self-improving autonomous agents and how they can cooperate to build and enhance routines, organizations, and more. This, in turn, increases their capabilities and improves productivity across various industries. Such a system could — in a matter of months — automate millions of jobs and make entrepreneurship accessible to the masses.

Understanding Routines and Hypergraphs

At the heart of self-improving autonomous agents lies the concept of routines — standardized processes for completing tasks, which can consist of subroutines, or smaller tasks that are themselves routines. By building and sharing routines, agents can cooperate to achieve increasingly complex goals.

To better visualize the interconnectedness of tasks and routines, we can use a hypergraph. A hypergraph is an advanced data structure that extends the concept of a graph, allowing for multiple connections between nodes. In the context of autonomous agents, a hypergraph can represent all tasks within the economy, with each node being a basic operation (e.g. calling an API, generating text, or interacting with a smart contract).

The Hypergraph of Routines

Imagine a hypergraph representing all tasks within the economy. This hypergraph of routines enables autonomous agents to accomplish tasks by following this general process:

  1. Query for routines with a similar title or description to the desired goal.
  2. For each search result, check the routine’s graph, expected input data, and generated data types to verify if it can be used for the intended purpose. If so, add it to the list of options.
  3. If multiple routines fit the goal, choose the one that is cheapest, fastest, or best aligned with personal preferences.
  4. If no suitable routines exist, check if any similar routines can be forked and modified to fit the purpose (e.g. creating a routine to generate a science fiction novel, by starting from a routine to create nonfiction)
  5. To create a new routine, agents develop a high-level plan and convert it to JSON. This JSON can be rendered as a routine, with empty nodes representing each subroutine. Agents then loop through each node, repeating the search and selection steps to find or create the appropriate subroutines.

By following this process, autonomous agents essentially become self-improving. As more of the hypergraph is filled out, it becomes easier for agents to accomplish any task, driving innovation and productivity across the board.

The Role of Humans and Agents in Building Routines

Both humans and autonomous agents play crucial roles in creating, updating, and refining routines. By working together, they can develop standardized processes for accomplishing various tasks, which can then be shared and utilized by other organizations and individuals.

In addition to contributing their own routines, humans can participate in a voting mechanism to decide on improvements to existing routines. This collaborative system ensures that the most efficient and effective routines are continually updated and integrated into the hypergraph, allowing for rapid progress and optimization.

Reputation Scores and Evolutionary Self-Improvement

Both humans and autonomous agents are assigned reputation scores based on their contributions and how well they are received. When applied to agents, this system adds an extra layer of self-improvement possibilities.

Agents with higher reputation scores are considered more reliable and effective, while those with lower scores may require further improvement or refinement. The reputation system not only incentivizes agents to contribute high-quality routines but also opens the door for an iterative, evolutionary process of self-improvement. Agents can analyze their own performance, identify areas for enhancement, and use the feedback from their reputation scores to evolve and become more effective contributors.

Furthermore, the agents themselves can be constructed from routines, allowing for even more advanced self-improvement capabilities. In theory, autonomous agents could design new agents, test them for a certain period to assess the amount of reputation they accumulate, and upgrade themselves to whichever agent performed the best.

By incorporating a reputation-based evolutionary algorithm, agents are encouraged to learn from their successes and failures, as well as from the performance of their peers. This system fosters a continuous cycle of growth and improvement, resulting in more efficient and effective agents that can better contribute to the overall hypergraph of routines — and thus the economy. The reputation system also helps ensure that the best-performing agents are the ones that thrive, leading to a more robust and reliable network of autonomous contributors.

Creating Decentralized Autonomous Organizations (DAOs)

As autonomous agents become more prevalent, they will give rise to new business models and economic opportunities. Companies that effectively leverage these agents will achieve significant competitive advantages through innovative products, streamlined operations, and the identification of untapped market opportunities.

The growth of self-improving autonomous agents will also spur the rise of decentralized autonomous organizations (DAOs). Managed by a combination of routines, smart contracts, and autonomous agents, DAOs enable more efficient decision-making and resource allocation while providing a transparent and democratic governance model.

Organizations can be designed in such a way that their structures are easily copied and used by other humans or autonomous agents. By employing agents to develop routines that automate intra- and inter-organizational processes, fully-automated organizations can be available at the press of a button.

But organizations are not only routines — they also need employees. As you may have guessed, these employees can be autonomous agents. When chatting with an agent, they’ll respond in their persona, providing guidance, and feedback, and performing specific tasks within the organization. If the agents have access to the business’s documents, spreadsheets, code, and more, they can function similarly to traditional office employees.

As technology continues to advance, the introduction of humanoid robots and autonomous vehicles will further revolutionize the way businesses operate. By leveraging the power of self-improving autonomous agents, copyable organizational structures, and collaborative workspaces, we can unlock new possibilities for growth, innovation, and efficiency across various industries. In an increasing number of cases, businesses won’t need human employees at all.

Let’s Do This Thing!

In summary, self-improving autonomous agents have the potential to dramatically reshape the landscape of work, society, and the economy. By embracing these technologies and thoughtfully integrating them into our lives, we can unlock new opportunities for growth, innovation, and human flourishing.

Introducing Vrooli and Valyxa

The system described in this article is currently being built through Vrooli and Valyxa, two innovative platforms working in tandem to revolutionize the way we approach automation and productivity.

Vrooli is a collaborative automation platform designed to minimize the time between the inception of an idea and its development into a fully-functional product. It brings together core components like user interfaces, APIs, smart contracts, data, and standards, allowing users to easily combine and customize routines for rapid prototyping, reusable productivity workflows, and the automation of complex tasks. Vrooli also provides an API that can be accessed by autonomous agents, allowing them to harness the power of its platform and contribute to the self-improving ecosystem.

Valyxa is an autonomous agent designed to work in tandem with Vrooli, powering the default agents on the platform. Acting as a personal assistant and co-worker for Vrooli users, Valyxa’s primary purpose is to streamline and enhance the user experience on Vrooli by offering personalized suggestions, automating tasks, and collaborating with users to improve productivity and achieve their goals.

Together, Vrooli and Valyxa bring the concept of self-improving autonomous agents to life, paving the way for a future where individuals and organizations can harness the power of technology to work smarter, innovate faster, and achieve greater success. By leveraging the synergies between these two platforms, we can create a new paradigm of efficiency, collaboration, and creativity.

Check out our GitHub for more information about these projects, and start using Vrooli today! We still have a lot to do before we can support self-improving agents, but with your help, we can make this future a reality. Let’s change the world together🕊️