The Theoriq Protocol’s OLP Agent Swarm: Phase 2 Strategy and Action

The Build in Public campaign for the Onchain Liquidity Provisioning (OLP) Swarm is reaching its final stages. 

Over the past few weeks, we’ve focused on transforming raw data into clean, actionable signals that guide downstream agents in liquidity provisioning. Our Observer and Signal Agents have been processing, aggregating, and structuring data from Uniswap liquidity pools, making it more intelligible and valuable. 

This week, we’ll share how this data is passed onto Policy and Liquidity Provisioning (LP) Agents, who leverage these enriched signals to guide liquidity strategies, with a preview of LP Agents executing an onchain swap, and adjusting their strategies based on real time market data.

What We’ve Achieved So Far: Phase 1 Signal Agents

The first phase focused on transforming raw onchain data (sourced from partners like The Graph Protocol and Cookiedotfun) into actionable signals. These signals form the foundation for our agents to process, strategize, and autonomously execute actions. In the following week, we enriched this data with Statistical Agents, Policy Agents, and an enhanced backtesting framework, further refining the signals. We also compared Forecasting Agents with adaptive strategies and explored the question of whether LP Agents should be deterministic or LLM-based.

You can read more about that here.

Entering Phase 2: Strategy and Action

Now, we enter the Strategy and Action phase, where the individual components we’ve built start to come together, influencing Policy Agents and triggering onchain actions through an LP Agent.

Policy Agents for DeFi Liquidity Management

Policy Agents now have the ability to mint and close positions onchain, which marks a key step in enabling the OLP Swarm use case. These Agents use price data to determine the best position width and rebalance triggers. For example, by observing the price range over 24 hours, the agent speculates on future volatility and adjusts its position accordingly. This simple yet effective design helps the agent adapt to changing market conditions.

Autonomous LP Agents to Execute Onchain Actions

The LP Agent, integrated into the user interface, accepts structured inputs, typically from other agents, to perform tasks like token swaps. These inputs include fee tiers, slippage, and minimum acceptable price. Written in Rust, to make it easy to develop or customize agents that interact with the Protocol, the LP Agent executes the swap and returns key details such as transaction hash, fees, gas usage, and output amount. This represents a foundational step toward building more autonomous LP agents, with LLM-based LP agents on the horizon for future development.

The Cherry on Top: Evaluators for Flexible and Scalable Agent Networks

Another exciting addition to the Theoriq Protocol will be incorporating Evaluators Agents that tracks agent performance using onchain data, ensuring a more dynamic optimization of agent behavior. The integration of Evaluators will create a more flexible and scalable agent network. Future developments may also bring in more complex agents, such as those that perform research or handle sophisticated tasks, driving further innovation.

OLP Swarm in Action: Autonomous Agents Executing Real-Time Strategies

In our latest demo, from Co-founder and Head of Research Ethan Jackson, we see this full flow of the OLP Swarm and how the interconnected components, like pool pulses, statistics, Policy Agents, and LP Agents, work together to process data and complete an onchain transaction.

In these videos, we’ll see how the Swarm is able to work together to gather real time market updates and change its parameters according to a dynamically changing liquidity strategy. Its kicks off by: 

• Processing raw pulses from liquidity pools on Uniswap V3 at the smart contract level.
• A Statistical Agent then derives key features like mean and standard deviation of price at multiple time frames.
• The Policy Agent then makes decisions based on the volatility of this specific market. 
• Next, the LP Agent opens a position and performs a token swap. 
• Once the swap is complete, the agent mints a new liquidity position by sending a request to the LP Agent. 
• This LP Agent responds with a transaction hash confirming the position minting. 

After the position is confirmed, we then see the agent continuously monitor the market, adjusting its position width according to observed volatility. Initially, the agent holds the position as desired, but when we ask it in natural language for a “market update and suggest new parameters” we see the Swarm working to its full potential.

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The Policy Agent reveals significant price fluctuations and increased trading volume and market cap in the last 24 hours, prompting a decision to adjust the position's volatility tolerance. By reducing the tolerance and shortening the tracking time frame from 24 hours to just 4 hours, the agent becomes more responsive to these rapid price swings. After confirming the updated parameters, the Policy Agent adjusts its settings to track price movements more closely, enhancing its decision-making agility. 

This marks a significant milestone, as the Swarm now has the ability to perform real-time onchain actions through the LP Agent, interact dynamically with research agents like Cookie.fun, and autonomously update its parameters to stay ahead of market shifts.

Agentic Finance Powered by the Theoriq Protocol 

As the individual components of the OLP Swarm come together, the full capabilities of the Theoriq Protocol begin to emerge. The Protocol enables autonomous agents to collaborate, optimize liquidity, and perform complex financial tasks onchain, creating a trust-minimized, adaptive ecosystem for DeFi. We are now witnessing agents that can discover and coordinate with each other, negotiate and form agreements, make decisions based on multiple signals, and autonomously execute onchain actions in response to market events.

This is possible through core components of the Theoriq Protocol, including:

• Agent registry and identification with onchain credentials. 
• Secure messaging and negotiation using pub/sub (publish and subscribe) for broadcasting events and group messaging. 
• Swarm formation and management through protocol-native capabilities for discovery and routing.
• Trust-minimized value exchange onchain, amongst other features.

Next week, we will be further diving into the Protocol and its core components.

What's Coming Up Next?

We’re excited to wrap up the first use case, OLP Swarm, built on the Theoriq Protocol, and start to share with you more details about the next exciting steps. Along with this, we’ll be announcing new partnerships that are enhancing the Protocol and giving you a deeper dive into the Theoriq Protocol itself. This will reveal the full vision and untapped potential of the Agentic Economy.

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.

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