A Retrospect of the Theoriq OLP Agent Swarm Development

Following our Build in Public ethos, we’ve committed to sharing our development journey of the Onchain Liquidity Provisioning (OLP) Swarm transparently, offering a clear view into our research, product iterations, and the challenges we have faced in building the future of Agentic Finance. 

The Onchain Liquidity Provisioning (OLP) Swarm, our first use case built on Theoriq, was also the first product we developed out in the open. It addresses a long-standing challenge across both DeFi and traditional finance: the efficient, autonomous management of liquidity. With Agent Swarms as a central pillar of the Agentic Economy, this use case marks a key step toward realizing that vision. More importantly, this development has brought our community closer to the core team at Theoriq.

As we look back on the early development of the OLP Swarm, we’ll break down the key milestones, highlight the lessons learned, and outline what’s next as we move toward a fully realized implementation of agent-driven liquidity provisioning.

Again, What is the Theoriq Protocol and the OLP Swarm?

As a quick refresh, the Theoriq Protocol is a decentralized, multi-agent protocol for AI-driven finance (AgentFI), designed to power autonomous agents such as liquidity managers, yield optimizers, and data agents to discover each other, communicate, form swarms, and collaborate on complex financial tasks in a trust-minimized environment. We developed this in two phases, each one reflecting the core components of the Swarm. 

Phase 1: Signal and Observer Agents

The first phase focused on transforming raw onchain data (sourced from partners like @graphprotocol and @cookiedotfun) into actionable signals. These signals form the foundation for our agents to process, strategize, and autonomously execute actions. 

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What Did We Build?

‍Observer Agents: monitor key market conditions, such as liquidity pool imbalances and price fluctuations, in real-time. They gather data from both onchain and offchain sources and broadcast any significant changes to downstream agents in the OLP Swarm. They act as the "eyes" of the network, enabling other agents to react autonomously.

‍Signal (statistical) Agents: analyze and process large sets of data to generate actionable insights and metrics, such as volatility and price percentiles, that help guide decision-making.

Every week Co-Founder and Head of Research Ethan Jackson broke down the latest updates with the community, sharing a “beneath the hood” look at the progress from research and development. During this phase he explained how Signal Agents refine raw data for downstream Action Agents, shed light on our retroactive backtesting framework,  and how the use of LLMs enables fast discovery and provides an intuitive interface for interacting with data. 

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What Challenges Did We Face?

In the second phase, we tackled the task of collating vast amounts of onchain data and organizing it in a way that could be processed efficiently by the OLP Swarm. We also had to answer the critical question: what is the most valuable data to influence liquidity strategies, the "needle in the haystack" of Web3 noise? We had to ensure agents accessed high-quality, relevant data, identified the key signals for decision-making, and built agents that could dynamically leverage and prioritize these signals to make effective, real-time decisions. 
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You can read more about Phase 1 here.

‍https://x.com/TheoriqAI/status/1908171877522067623 

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Phase 2: Strategy and Onchain Actions 

The second phase focused on passing the signals gathered by the Signal and Observer agents to the next agents in the Swarm—Policy and Liquidity Provisioning (LP) Agents. These agents then use the signals to influence liquidity strategies, which are executed onchain. 

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What Did We Build?

‍Policy Agents: analyze data and signals from Observer Agents to define strategic decisions for liquidity provisioning. They determine when and how to adjust liquidity positions based on market conditions, creating and refining strategies.

‍Liquidity Provisioning (LP) Agents: autonomously execute onchain actions to adjust liquidity positions based on strategies set by Policy Agents. They respond to real-time market data and signals, managing liquidity across markets to optimize returns, minimize slippage, and mitigate risks. 

Ethan returned to demo a 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 adjust its strategy. Below, you'll see how the Swarm gathered real-time market updates and changed its parameters according to a dynamically changing liquidity strategy.

https://x.com/TheoriqAI/status/1915459414536200499 

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What Did We Learn?

In the second phase, we encountered the unpredictable realities of onchain markets — network congestion, outages, and other external factors outside the team’s control added complexity to building and maintaining live systems. These conditions made outcome prediction and stability more challenging.

You can read an in-depth blog about this phase below.

https://x.com/TheoriqAI/status/1915506434328109378 

So, What’s Next?

Based on conversations with industry leaders and a refreshed vision for the year ahead, we're excited to evolve this initial use case into a series of Agent Swarms built exclusively on the Theroiq Protocol, that are focused on onchain liquidity management. With the first swarm concentrating on automating strategies for DEX liquidity, our MVP will specifically focus on liquidity provisioning using an LP manager agent coordinating the swarm.

We’re exploring how to integrate key technologies in the protocol and exploring proven stacks like vaults and LP strategy solutions to maximize the swarm's capabilities. Stay tuned for more updates on that front in the next few weeks.

During the next development phase, we’ll be launching social campaigns, activating partnerships, and sharing more about the interconnectedness of the Theoriq Protocol and our wider ecosystem that will drive its growth and adoption.  

–Stay tuned as we continue to push forward, share milestones, and engage our community in shaping the future 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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