Multi-Agent Architecture: Four Implementation Approaches

The core multi-agent pattern is universal. What changes is how you orchestrate it — from prompt chaining to dynamic model-driven coordination.

Whether you're building with OpenClaw, Python, LangChain, or custom code, the fundamental multi-agent pattern is identical: a coordinator receives a task, delegates to specialists, aggregates their findings, and returns synthesis. The architecture doesn't change. What changes is the mechanism.

The Universal Pattern

Every implementation follows the same flow: coordinator receives task → delegates to subagents → subagents return findings → coordinator synthesises. The variables are how you define specialists, how they communicate, and whether decisions are made at design time or runtime.

Four Approaches, One Architecture

Approach 1: Prompt-chained agents

Closest to traditional automation. You craft a series of prompts manually. Each prompt passes findings from the previous step. The sequence is fixed. OpenClaw exemplifies this: define specialist templates, define sequence, let the system loop through them. Transparent and debuggable, but rigid — the coordinator can't dynamically skip agents or adapt based on findings.

Approach 2: Programmatic orchestration

Claude acts as the coordinator. It reasons about the query, decides which subagents to invoke, what context to pass, and whether output is sufficient. The Task tool spawns subagents. This is model-driven orchestration — adaptive, flexible, handles novelty well. Harder to debug, but fundamentally more powerful.

Approach 3: Framework-based (LangChain, CrewAI, AutoGen)

Pre-built abstractions handle the plumbing. You define agent roles and tasks. The framework orchestrates. This abstracts away the underlying mechanics but applies the same principles — the concepts map directly to what the programmatic approach does manually.

Approach 4: Autonomous agents

Persistent agents running on schedules or event triggers. No human initiates the query. The loop mechanics remain identical — stop_reason, tool execution, history management — but the trigger is automated. Higher stakes because no human watches the execution.

Static vs Dynamic Orchestration

The key difference between approaches is decomposition timing. Prompt chaining fixes specialists at setup time. Model-driven approaches analyse each query and decide which specialists are needed dynamically. Simple queries invoke fewer agents. Complex queries invoke more.

Dynamic also means iterative refinement. After synthesis, does the coordinator evaluate gaps and re-delegate? Or is the first answer final? Robust architectures loop back.

The Discipline That Matters

Across all approaches, one principle is immutable: subagents only know what they are told. The coordinator must know everything and inform each specialist exactly what it needs. This discipline — explicit context passing, structured metadata (claim, source, URL), attribution end-to-end — is what separates production systems from prototypes.

Pick any approach. The architecture is the same. The discipline and dynamism determine whether it works.