The Shift from Single-Pass to Iterative Agent Design

Most early LLM applications treated model outputs as final. But production agentic systems require continuous refinement. The key insight is that agents become significantly more capable when they can critique their own outputs, access external tools, decompose complex tasks, and delegate to specialists. This mirrors how human teams solve problems: through iteration, tool usage, structured planning, and collaboration.

Core Reasoning Patterns in Agent Architecture

Modern agent frameworks leverage four foundational patterns that unlock advanced reasoning capabilities:

Pattern Comparison

These patterns are not mutually exclusive. Production systems often combine multiple patterns, with reflection embedded in planning loops or tool use coordinated across agent teams.

Implementing Reflection for Output Quality

Reflection patterns introduce a critic-generator loop. The agent generates an initial response, then evaluates it against quality criteria like accuracy, completeness, or tone. Based on the critique, it refines the output iteratively. This is particularly effective for tasks requiring nuanced judgment, such as generating documentation, debugging code, or crafting marketing copy. The pattern requires defining clear evaluation rubrics and limiting iteration depth to prevent infinite loops. In practice, 2-3 reflection cycles yield diminishing returns for most tasks.

Applied Insight: When to Apply Each Pattern

Use Reflection when output quality matters more than speed, especially for creative or analytical work. Tool Use is essential when agents need real-time data or computational capabilities beyond the base model. Planning works best for multi-step workflows where sequencing matters, like data pipelines or deployment automation. Multi-Agent Collaboration shines in complex domains requiring distinct expertise, but introduces coordination overhead. Start simple with single-pattern implementations before layering complexity.