How to Plan Execution for AI Agents

An AI agent execution plan is a blueprint that translates a user's intent into a series of discrete, executable operations. Unlike a simple prompt, a plan anticipates dependencies, manages state, and handles potential failures. This is critical for complex tasks in Web3, such as on-chain arbitrage, multi-step DeFi interactions, or automated data analysis, where a single misstep can result in financial loss or incorrect outcomes. Effective planning moves agents from reactive chatbots to proactive problem-solvers.

The core components of a plan include a defined objective, a list of required tools or APIs (like web3.js for blockchain calls or axios for data fetching), a logical sequence of steps, and conditional logic for error handling. For example, a plan to "swap ETH for USDC on Uniswap" would involve steps like: 1) Check wallet balance, 2) Get current gas prices, 3) Approve token spend, and 4) Execute the swap. Each step's success or failure determines the next action.

Developers implement planning using frameworks like LangChain with its Plan-and-Execute agent executor, or AutoGen with its conversational programming model. These frameworks provide the scaffolding to define tools, manage memory, and orchestrate the step-by-step logic. A basic plan in code often starts as a structured prompt or a graph of nodes, where each node represents a function call with specific inputs and outputs, creating a transparent and debuggable execution trace.

Key challenges in execution planning include handling non-deterministic environments (like fluctuating gas fees or failed transactions), managing context limits for long-running tasks, and ensuring cost-efficiency by minimizing unnecessary LLM calls or on-chain operations. Best practices involve building idempotent steps, implementing robust retry logic with exponential backoff, and validating the output of each step before proceeding to maintain the integrity of the entire operation.

For Web3 developers, integrating execution planning is essential for building reliable autonomous agents. Start by mapping out common user journeys, identifying the discrete smart contract calls and data queries they require. Use existing agent SDKs to prototype, and always simulate plans in a testnet environment before mainnet deployment. The goal is to create agents that not only understand what to do but can reliably figure out how to do it, step by step.

Execution planning defines the agent's decision-making loop—the process by which it breaks down a high-level objective into a sequence of actionable steps, selects the right tools, and adapts to new information. At its simplest, this follows a pattern: Perceive the current state and goal, Plan the next action, Act using a tool or API, and Observe the result before repeating. This cycle is often implemented using frameworks like LangChain's AgentExecutor or the ReAct (Reason + Act) prompting pattern, which interleaves natural language reasoning with tool calls.

The planner's intelligence is dictated by the orchestrator LLM (e.g., GPT-4, Claude 3) and the tools at its disposal. You must provide the agent with a well-defined toolkit, such as a function to query a blockchain RPC, call a smart contract, or fetch data from an API like Dune Analytics. The planner's prompt includes descriptions of these tools, enabling the LLM to reason about when and how to use them. For example, a DeFi agent's plan to "swap ETH for USDC on Arbitrum" might involve the steps: check_eth_balance, get_uniswap_quote, and execute_swap.

Planning must account for state management and error handling. An agent maintains context across steps, often through a working memory or a vector database for long-term recall. Robust planners include fallback logic for failed actions, such as retrying with different parameters or invoking a human-in-the-loop. In Web3, this is critical for handling transaction reverts or RPC timeouts. Implementing a planning loop with explicit error checks and state validation prevents agents from getting stuck in infinite loops or executing invalid sequences.

Advanced planning involves multi-agent coordination and recursive task decomposition. For complex objectives like "deploy a token and create a liquidity pool," a top-level planner might delegate subtasks to specialized sub-agents (e.g., a deployment agent, a liquidity agent). This hierarchical planning can be orchestrated using frameworks like AutoGen or CrewAI. The key is defining clear interfaces and success criteria between agents, ensuring the overall execution plan remains coherent and auditable across the entire workflow.

To implement a planner, start by defining your agent's core loop in code. Using LangGraph or a simple while loop, you can structure the process to evaluate a stop condition (goal achieved or error limit reached). Each iteration should call the LLM with the current state, available tools, and execution history. Log every decision and outcome to a structured format like JSON for debugging and analysis. This traceability is essential for refining prompts and tool definitions, which directly impact the planner's reliability and effectiveness.

Agent execution planning is the process of breaking down a high-level user request into a sequence of discrete, actionable steps. Unlike simple function calls, agents must reason about dependencies, validate intermediate states, and adapt to new information. Common planning frameworks include Chain of Thought (CoT) for step-by-step reasoning and Tree of Thoughts (ToT) for exploring multiple solution paths. For example, an agent tasked with "analyze the top DeFi protocols" must first plan to fetch current TVL data, then retrieve protocol contracts, and finally compute metrics—each step contingent on the previous one's success.

Implementing a plan requires a clear action loop. A typical loop involves: 1) Task Decomposition using an LLM to split the goal into subtasks, 2) Tool Selection where the agent picks the appropriate function (e.g., fetch_price_data, call_smart_contract), 3) Execution of the tool with proper parameters, and 4) Observation & Reasoning to evaluate the output and decide the next step. Libraries like LangChain's PlanAndExecute executor or AutoGPT's task management system formalize this loop. The key is ensuring each action's output provides the necessary context for the subsequent step.

For reliable execution, plans must handle state management and error recovery. Agents should maintain a working memory of completed steps, results, and encountered errors. This state informs replanning when a tool fails or returns unexpected data. For instance, if an on-chain RPC call times out, the plan should include a fallback to a different provider. Using a graph-based representation of tasks, where nodes are actions and edges are dependencies, allows for dynamic re-ordering. Frameworks like Microsoft's TaskWeaver and research on ReAct (Reasoning + Acting) highlight the importance of interleaving planning and action for complex, real-world tasks.

Advanced planning incorporates verification and validation steps. Before executing a sensitive action—like signing a transaction—an agent should simulate or dry-run the step to predict outcomes. In Web3, this could involve using a eth_estimateGas call or a forked testnet simulation via tools like Foundry or Tenderly. Planning should also integrate resource constraints, such as gas budgets, API rate limits, and token context windows. Explicitly modeling these constraints prevents agents from getting stuck in infinite loops or incurring unexpected costs, moving from naive task lists to robust, production-ready execution flows.

An execution plan is a deterministic sequence of actions and decisions that an AI agent follows to achieve a goal. Unlike a simple prompt, it breaks down complex tasks into manageable steps, incorporates conditional logic, and can interact with external tools and data. Think of it as the agent's internal program, specifying what to do, when to do it, and how to handle different outcomes. This structure is crucial for reliability, reproducibility, and enabling agents to operate autonomously in dynamic environments like DeFi protocols or on-chain analytics.

The first step is goal decomposition. Start with a clear, high-level objective (e.g., "Execute a profitable DEX arbitrage") and break it into sub-tasks. For an arbitrage agent, this includes: monitoring multiple DEX liquidity pools for price discrepancies, calculating gas costs and potential profit, securing flash loan approval, and finally executing the swap. Each sub-task should be atomic and have a clear success/failure condition. This modular approach makes the plan easier to debug, test, and adapt for different use cases.

Next, define the action primitives and decision logic. Action primitives are the basic, executable operations your agent can perform, such as fetch_price(protocol, pair), calculate_arbitrage_opportunity(), or execute_swap(router, amount). The decision logic, often implemented as if/else statements or a state machine, dictates the flow. For example: if (potential_profit > min_threshold && gas_cost < profit * 0.1) { proceed_to_execute(); } else { wait_and_monitor(); }. This logic is the core intelligence of your plan.

Finally, implement the plan with tool integration and error handling. Modern agent frameworks like the OpenAI Assistants API, LangChain, or AutoGen provide structures for defining tools and execution flows. Your plan must specify which external APIs or smart contracts it will call. Crucially, incorporate robust error handling at each step: check for transaction reverts, API timeouts, and slippage tolerance breaches. A well-architected plan logs each step, allows for safe cancellation, and can revert to a safe state if a critical step fails, protecting user funds and ensuring system resilience.

Effective AI agent execution requires a structured approach. Start by defining clear system boundaries and failure modes. For example, an agent handling on-chain transactions must have predefined gas limits, slippage tolerances, and fallback routines for failed transactions. Use a state machine pattern to manage the agent's lifecycle, with states like IDLE, PLANNING, EXECUTING, and ERROR. This makes the agent's behavior predictable and debuggable.

Next, implement the planning logic. For a DeFi agent, this could involve querying multiple liquidity sources like Uniswap V3 and Curve to find the optimal swap route. Use a modular design where the planner is separate from the executor. The planner, using data from oracles like Chainlink, outputs a structured ActionPlan object. This object should be serializable and include all necessary parameters—target contract addresses, calldata, and value—before any on-chain interaction occurs.

Focus on security and monitoring. All agent actions should pass through a simulation stage using tools like Tenderly or a local fork via Foundry's anvil before live execution. Implement comprehensive logging and alerting. For instance, log every plan, simulation result, and on-chain transaction hash to a service like Datadog. Set up alerts for abnormal conditions, such as a transaction costing more than 200% of the estimated gas or a swap resulting in extreme negative slippage.

To iterate and improve, establish a feedback loop. Analyze logs to identify common failure points—was it a faulty price oracle, network congestion, or an edge case in your planning logic? Use this data to refine your agent's decision trees and risk parameters. Consider implementing A/B testing for different strategy parameters in a controlled, testnet environment before deploying updates to mainnet.

For further learning, explore advanced frameworks and research. Study the architecture of established agent systems like OpenZeppelin Defender for automation or Yearn's keeper networks. Read academic and industry papers on autonomous agent design and mechanism safety. The goal is to move from a script that performs a task to a resilient, self-correcting system capable of operating in the adversarial and unpredictable environment of decentralized networks.