The Theoriq Protocol’s OLP Agent Swarm: Progress Update and Adapting Hypotheses

Last week, we introduced the Onchain Liquidity Provisioning (OLP) swarm built on Theoriq’s Protocol, focusing on the first phase and the development of Signal Agents—agents that provide the OLP swarm with context from a variety of data sources. This week, we're highlighting our progress, sharing how we're further enriching last week's signals for downstream agents, and reflecting on our hypothesis regarding the potential use of forecasting agents.

For a deeper dive into what we highlighted last week, you can read the article below.

The Theoriq Protocol and the OLP Swarm

The Theoriq Protocol is a decentralized, multi-agent protocol for AI-driven finance (AgentFI), designed to enable autonomous agents such as liquidity managers, forecasting bots, and data providers to communicate, discover opportunities, form “swarms,” and collaborate on complex financial tasks in a trust-minimized environment. The OLP swarm is the initial use case, and consists of self-improving agents that autonomously optimize liquidity positions using real-time data, forecasts, and historical metrics. The swarm’s composable architecture is designed to be flexible and scalable, responding to dynamic market conditions.

At the heart of the OLP swarm is its ability to ingest signals from various data sources, processing this information to create liquidity strategies and empower agents to execute onchain tasks. This phase is crucial for formulating liquidity strategies that will be actioned by a range of users in the DeFi ecosystem.

Adapting to the Next Development Phase of the OLP Swarm

As part of our "Build in Public" campaign, we aim to transparently share our progress and highlight the decisions and challenges we face in creating solutions for problems that don’t have ready-made answers.

Last week, we introduced observer/signal agents that process raw data into actionable signals for the OLP swarm. These signals are then enriched into insights, such as computed statistics, which help generate observed price ranges for the pool over a specific time period.

The initial hypothesis was that the next phase would involve a forecasting agent using advanced machine learning techniques to predict liquidity pool parameters, such as volume and price, across different timeframes. However, we also saw the potential of a more practical, data-driven approach, combining real-time market data, technical indicators, and adaptive agent-based strategies. After thorough iterations, the team has decided to lean towards the latter, reserving the development of forecasting agents for a later stage in the product cycle.

This choice is rooted in the fact that forecasting in volatile markets like cryptocurrency is inherently challenging. While forecasting models provide insights, their reliability often falls short in unpredictable environments. This approach aligns better with the core principles of the Theoriq Protocol, which emphasizes the integration of multimodal data streams and encourages third-party developers to contribute unique signal agents.

By incorporating these data sources, we can promote a dynamic ecosystem of agents that evolve with market trends, offering greater flexibility and scalability in the long term. Ultimately, this decision highlights the importance of adaptability in the fast-paced world of DeFi.

Below you will find a progress update from Co-Founder and Head of Research Ethan Jackson, where he provides an overview of the swarm architecture and the components that are enriching signals for agents downstream in the OLP swarm.

Retroactively Backtesting Policy Agents

The team has also been working on a backtesting framework that retroactively tests different liquidity provisioning policies to evaluate their effectiveness in a specific pool. One example is a policy that is sensitive to volatility, adjusting liquidity position widths based on price movements. The framework allows for metric-based comparisons to identify strategies that maximize yield and outperform a simple holding strategy, while minimizing risk.

By treating this as a strategy search problem, the team is exploring various approaches to liquidity management to optimize certain parameters, such as fee accumulation without unnecessary risk.

LP Agents: Should they be Deterministic or LLM Based?

Another core part of the OLP swarm are Liquidity Provisioning (LP) agents. The team is currently evaluating two primary approaches for optimizing LP agents: deterministic policies and Large Language Model (LLM)-based agents.

Deterministic policies are the status quo in the industry as they offer a fast and cost-effective method for experimenting with different strategies. These policies are based on structured data inputs like price, volume, and volatility, and produce predictable actions, such as opening, closing, or rebalancing liquidity positions. While deterministic approaches allow for quick testing and clear understanding, they can lack flexibility and may not be suitable for rapidly changing market conditions.

Although deterministic policies do have their benefits, the team has hypothesized that applying agent specific solutions, like LLM agents, could generate a more effective solution in DeFi.

Unlike deterministic policies, LLM agents can process both structured and unstructured data, making them far more adaptable. These agents can incorporate a range of data sources, from real-time market signals to third-party contributions, allowing them to respond more effectively to unexpected market shifts. By incorporating LLM agents into the OLP swarm, we aim to build a more dynamic, evolving LP agent framework that moves beyond rigid, pre-defined rules.

‍

This focus on LLM-based strategies fits within the broader vision of creating an ecosystem of adaptable agents that continuously evolve as new data sources are introduced. The move also reflects our commitment to building more intelligent, flexible solutions that can respond effectively in the DeFi market.

This focus on LLM-based strategies fits within the broader vision of creating an ecosystem of adaptable agents that continuously evolve as new data sources are introduced. The move also reflects our commitment to building more intelligent, flexible solutions that can respond effectively in the DeFi market.

What’s Next for the Build in Public Campaign?

The past few weeks are moving fast, and the team is iterating quickly to bring the OLP swarm to life. As we approach the half way point in our campaign, we have used this week to reflect and refine key components of the OLP swarm, and incorporated our takeaways into the  foundational elements of the Theoriq protocol.

Next week, we take these learnings and apply them to the “brains” of the OLP swarm, Phase 2: Strategy Agents.

Advancing Autonomous Liquidity Provisioning in DeFi

The evolution of the OLP swarm exemplifies how “building in public” can accelerate innovation. As we continue to iterate and refine our strategies, we remain committed to developing a dynamic protocol that can handle complex financial use cases such as OLP and advance the future of the agentic economy.

By staying flexible and adaptive in our research and development, we are uncovering exciting implementations of AI methodologies that will make existing DeFi ecosystem more intuitive and intelligent.

About Theoriq

Theoriq is committed to building a responsible, inclusive, and consensus-driven AI landscape in Web3. At the forefront of integrating AI with blockchain technology, Theoriq empowers the community to leverage cutting-edge AI Agent collectives to improve decision-making, automation, and user experiences across Web3.

Theoriq is a decentralized protocol for governing multi-agent systems built by integrating AI with blockchain technology. The platform supports a flexible and modular base layer that powers an ecosystem of dynamic AI Agent collectives that are interoperable, composable and decentralized.

By harnessing the decentralized nature of web3, Theoriq is unlocking the potential of collective AI by empowering communities, developers, researchers, and AI enthusiasts to actively shape the future of decentralized AI.

Theoriq has raised over $10.4M and is backed by Hack VC, Foresight Ventures, Inception Capital, HTX Ventures and more, and have joined start-up programs with Google Cloud and NVIDIA.

‍