AI Agents & Tool Use implementation checklist

Verify that all tool definitions include strict JSON schemas with required fields and type constraints to prevent LLM hallucination of arguments.

Implement a standard format for tool error messages that provide the LLM with actionable feedback on how to fix incorrect parameters.

Ensure no two tools have overlapping semantic descriptions to prevent the model from selecting the wrong tool for a given task.

Implement a versioning system for tool schemas to allow for rolling updates without breaking active long-running agent sessions.

Create a flag for each tool to simulate execution, returning a mock response to test agent planning without side effects.

Set a maximum number of steps (e.g., 10-15) for the agent loop to prevent infinite loops and runaway costs.

Implement logic to detect and stop the agent if it calls the same tool with the same arguments multiple times in a single session.

Monitor for repetitive thought patterns in the LLM output and trigger a forced context reset or human intervention if detected.

Implement a strategy for pruning or summarizing history when the agent's scratchpad approaches the model's token limit.

Define explicit exit conditions for the agent, including when a goal is unreachable or requires unavailable tools.

Pass a unique trace ID through every step of the agent loop to link LLM prompts, tool calls, and final outputs in logs.

Log the cumulative token count and cost for every agent run to identify high-cost workflows and optimize prompt length.

Record the execution time of each tool call to identify bottlenecks in the agent's external integrations.

Store the exact prompt sent to the LLM at each step (including few-shot examples) for post-incident debugging.

Provide a real-time log or graph view for developers to see the agent's current 'thought' process and tool outputs.

Configure specific tools (e.g., payments, deletions) to require manual approval before the agent can execute them.

Run tools that execute code or shell commands in isolated containers with restricted network and filesystem access.

Ensure the API keys used by the agent have the minimum necessary scopes required for the tools provided.

Validate and sanitize LLM-generated tool arguments to prevent SQL injection or prompt injection via tool inputs.

Implement a middleware layer to detect and mask Personally Identifiable Information in agent logs and tool payloads.

Define a strict schema for the shared memory or state object used when handing off tasks between different specialized agents.

Verify that the 'Router' agent has clear criteria for when to delegate a task to a sub-agent vs. handling it directly.

Establish priority levels for agents that may attempt to access or modify the same resource simultaneously.

Implement checks to detect if two agents are waiting for each other's output before proceeding.

Verify that handoffs between agents are authenticated and cannot be triggered by external spoofed requests.

Implement a TTL-based cache for deterministic tool outputs to reduce redundant LLM processing and external API calls.

Configure the agent to execute multiple independent tool calls in parallel when the LLM identifies several needed actions.

Use a smaller, cheaper model for simple tool selection and reserve larger models for complex planning and synthesis.

Ensure tool outputs are streamed to the agent's context as they finish to improve perceived user responsiveness.

Set hard daily or per-user budget limits that automatically disable agent execution when exceeded.