Our tech team have been hard at work building the future of AI-driven Web3 but somehow, they have still found time to bring you a deep dive into the tech that's fascinating them as they work! Check out this latest foray into the intriguing forest of behaviour trees and their potential application in AI agents' performance for Web3.
AI-driven execution is advancing beyond static automation. As autonomous AI agents take on more responsibility in transaction execution, asset management, and decentralised governance, their decision-making frameworks must be highly adaptable, scalable, and transparent.
Behaviour trees, originally developed for robotics and game AI, offer a structured, modular approach to decision-making that has been widely adopted in industries requiring dynamic execution and real-time responsiveness. Their ability to organise complex behaviours into hierarchical structures makes them particularly well-suited for adaptive AI agents, a growing area of focus in Web3 applications.
As AI automation in blockchain ecosystems continues to develop, interest in structured AI decision-making frameworks is growing. With ongoing advancements in agent-based execution, modular AI development, and decentralised AI-powered systems, exploring how structured methodologies such as behaviour trees can support these goals is increasingly relevant.
Griffin AI, for example, recently introduced the Transaction Execution Agent (TEA), designed to streamline blockchain transactions, and is preparing to launch the AI Agent Builder, which will allow for custom AI-driven execution frameworks. While behaviour trees are not currently an explicit focus, structured decision models align with the broader effort to create AI-driven execution models that improve efficiency in blockchain interactions.
This article explores how behaviour trees function, their architectural advantages, trade-offs compared to other AI decision models, and their potential relevance in AI execution frameworks within Web3.
The role of behaviour trees in AI-driven execution
Behaviour trees structure AI decision-making into hierarchical nodes, allowing agents to break down execution into modular, reusable sub-tasks. Unlike traditional Finite State Machines (FSMs), which require explicit transitions between every possible state, behaviour trees allow dynamic priority adjustments, error containment, and concurrent execution management.
At a high level, behaviour trees consist of four primary components.
Action nodes execute specific tasks such as sending a transaction, approving a trade, or adjusting a liquidity position.
Condition nodes check execution requirements, ensuring that gas fees remain within a defined threshold or that a liquidity pool is sufficiently deep.
Composite nodes determine execution flow using selector and sequence logic, allowing an agent to dynamically choose the best action based on current blockchain conditions.
Decorator nodes modify execution behaviours, such as repeating tasks at set intervals or altering how success and failure conditions propagate.
In decentralised applications, execution logic must be flexible enough to adapt to changing blockchain environments. For example, an AI agent performing a DeFi trade must continuously reassess price movements, liquidity, and transaction costs. The modular nature of behaviour trees makes them well-suited for AI-driven execution frameworks where real-time responsiveness and adaptable decision-making are essential.
As AI-powered automation continues to develop, structured frameworks that support adaptive execution logic will be essential for improving efficiency and reducing friction in blockchain interactions. Whether through behaviour trees or alternative structured methodologies, enabling modular and responsive AI execution will be an important consideration in AI-native Web3 applications.
Why behaviour trees are a strong fit for Web3 automation
AI execution in Web3 requires high modularity, real-time reactivity, and computational efficiency. When compared to FSMs and machine learning-based execution models, behaviour trees offer a structured approach that balances adaptability with reliability.
One of the biggest challenges in AI-driven execution frameworks is ensuring that new behaviours can be integrated seamlessly without breaking existing logic. Behaviour trees allow for modular decision-making, meaning that sub-trees can be updated independently without requiring extensive revisions to the entire execution model. This is particularly relevant in evolving blockchain ecosystems, where protocol updates, market changes, and governance decisions can require AI agents to adjust their execution logic dynamically.
While FSMs often execute faster in simple cases, they become difficult to scale when applied to complex, adaptive decision-making scenarios. Behaviour trees, by contrast, allow AI agents to dynamically adjust execution paths, ensuring that transactions are processed in a more efficient and context-aware manner.
The ability to evaluate and respond to blockchain conditions in real time makes behaviour trees an area of interest in AI execution models that must operate under variable on-chain conditions.
Another important advantage of behaviour trees is their error containment capabilities. Traditional AI execution models may suffer from cascading failures, where a single failed transaction or miscalculated decision disrupts an entire execution workflow.
Behaviour trees structure execution in a way that isolates failures, enabling alternative decision paths to be attempted without affecting the entire system. In decentralised finance and on-chain governance, where autonomous execution must be robust and failure-resistant, structured decision models that contain errors and optimise failover strategies will be valuable.
As AI automation expands in areas such as DeFi execution strategies, agent-driven asset management, and AI-enhanced governance, ensuring that decision-making frameworks are structured, modular, and adaptable will be an important consideration. Behaviour trees are one of several methodologies that offer a way to manage execution complexity in AI-driven Web3 applications.
Adaptive execution: Responding to blockchain network conditions
The ability to adjust execution strategies based on real-time blockchain data is a defining requirement for AI-driven financial agents. Blockchain conditions such as gas fees, liquidity availability, governance updates, and price fluctuations change by the second, meaning that execution models must be able to dynamically adapt to these conditions.
An AI execution agent using behaviour trees could, for example, evaluate gas fees before executing a transaction and delay execution if conditions are unfavourable. Similarly, it could monitor market liquidity before committing to a trade, ensuring that transactions are not executed in an inefficient or high-slippage environment.
If a governance proposal passes that changes a protocol’s transaction fee structure, an AI agent could dynamically adjust execution parameters without requiring a manual update to its logic.
As AI-powered execution frameworks continue to develop, real-time responsiveness and structured execution logic will become increasingly important. While behaviour trees are one possible approach, the broader industry trend is towards adaptive execution models that can balance autonomy with transparent, rule-based decision-making.
AI execution frameworks and Python-based implementations
The Python ecosystem has played a key role in accelerating the development of behaviour tree frameworks, providing robust libraries that support modular, scalable AI execution. Among the most widely used libraries, py_trees has become a standard for robotics and autonomous decision-making, offering real-time tree visualisation tools and asynchronous tick management.
For AI applications requiring cross-platform execution, behavior3py has gained traction due to its JSON serialisation capabilities and support for multi-agent execution contexts. These libraries have enabled the development of more flexible, adaptive AI execution models, particularly in multi-agent financial automation and cross-chain asset transfers.
Performance optimisation remains a key challenge in high-frequency execution environments. Techniques such as node caching, hybrid FSM-BT architectures, and JIT compilation have been explored to improve execution speeds while preserving the modularity and transparency that behaviour trees offer. These approaches contribute to efficient, high-performance decision execution in AI-driven automation frameworks.
The future of AI-driven execution in Web3
As AI execution frameworks continue evolving, behaviour trees provide a scalable and structured approach to automation. With Web3 increasingly exploring agent-driven automation, execution models that support adaptive decision-making and modular execution logic will be a key part of scaling AI-native financial and governance systems.
While there are multiple approaches to structuring AI-driven execution in Web3, behaviour trees offer a structured framework worth exploring. For those working on AI execution methodologies, decentralised AI automation, and intelligent agent-based systems, structured decision models like behaviour trees provide a valuable reference model for AI automation in decentralised environments.
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