how do I give an ai agent a reliable planning loop?

Building a reliable planning loop for an AI agent requires three specific components: strategic selection, structured output, and self-correction. Here is how to implement that approach.

### 1. Select the Planning Paradigm Based on Task Complexity No single strategy works everywhere. Your choice depends on workflow type and resource constraints like latency and cost.

* **Long-horizon, repetitive tasks:** Use **Plan-and-Execute**. Decoupling strategic planning from execution improves accuracy per dollar. * **Long-document tasks:** Use **ReAct**. It handles these well but struggles with complex dependencies. * **Verifiable tasks (e.g., coding):** Use **Reflexion**. Self-critique loops boost accuracy here.

All strategies demand significant inference-time compute. You must account for cost in production environments.

### 2. Format Execution Results for Reliability The LLM needs machine-readable outputs to process action results reliably. Enforce structured formats.

* **Structured Formats:** Output **JSON** or **TOON**. Enforce the schema at the model layer using Pydantic or `response_format` to stop data drift. * **CLI Interactions:** Use compact, token-efficient formats. They must provide definitive empty states and structured errors on stdout. * **Reporting:** Use progressive disclosure with collapsible markdown. Keep headers at h3 or lower. Drop emojis and conversational fillers. * **Efficiency:** Include pre-computed aggregates in the output to reduce follow-up calls.

### 3. Instruct the LLM to Revise Plans Using Self-Correction Use specific prompts to guide revision without over-criticizing correct answers.

* **Confidence Assessment:** Apply the **If-or-Else (IoE)** method. Let the model assess confidence before revising. This stops unnecessary changes to correct answers. * **Structured Verification:** Use **Program-driven approaches (like ProgCo)**. Generating pseudo-code for verification logic offers more structured reasoning than natural language prompts. * **Verification Technique:** Try the **PROCO framework**. It uses substitute verification by masking key conditions to see if the model still derives the correct answer. * **Settings:** Use neutral prompts and **zero-temperature settings** for accurate self-correction.

### Pending Action This answer provides architectural and prompt-engineering guidelines for a reliable planning loop. Per governance rules, no write, send, create, delete, or payment actions are taken. If you want to implement this in **Liminality** or **Motherboard**, confirm the specific integration steps you wish to proceed with.