Theory: Single Agent Workflows

Overview

Planner-executor loops formalize how an agent thinks and acts over multiple steps. Instead of one-shot completion, the agent forms a plan, executes a bounded action, observes results, and decides the next move.

This loop improves reliability because final answers are backed by evidence gathered during execution. It also makes failure modes visible: weak plans, repeated actions, and low-information steps.

Learning Ladder

Level	Focus	Outcome
Beginner	Plan-act-observe cycle	Understand loop structure
Intermediate	Stop criteria and evidence checks	Avoid weak or premature answers
Advanced	Adaptive progress heuristics	Better efficiency under uncertainty

Core Concepts

Beginner Foundation

• Separate planning, execution, and observation into explicit phases.
• Record evidence after each step before choosing next action.
• Bound maximum steps and retries to avoid runaway loops.
• Preserve a concise trace so decisions are reviewable.

Intermediate Mechanics

• Add evidence sufficiency checks before finalizing outputs.
• Detect repeated or low-yield actions and trigger fallback.
• Include uncertainty notes when evidence is incomplete.
• Use stop criteria based on both quality and cost budgets.

Advanced Production Patterns

• Score per-step information gain to prioritize next actions.
• Stop early when expected gain drops below threshold.
• Escalate uncertain outcomes to optional review workflows.
• Compare loop strategies with eval suites before production rollout.

Key Takeaways

• Effective loops optimize for evidence quality, not step count.
• Explicit stop criteria prevent both under-solving and over-computing.
• Confidence-aware escalation reduces risk in ambiguous cases.