Multi-Agent Orchestration Patterns in AACFlow: 4 Approaches for 2026

Pattern 1: Sequential Chain

The simplest pattern. Agent A produces output, Agent B takes that output as input, Agent C takes B's output, and so on. Each agent has a narrow, well-defined role.

When to use it: linear transformation pipelines where each step refines the previous output. Document summarisation → translation → tone adjustment. Data extraction → enrichment → formatting.

In AACFlow: connect AI Agent blocks with direct edges. Each block's output variable (response) feeds the next block's system prompt or user message via {{previous_agent.response}}. The workflow canvas shows the chain visually, making handoff points obvious.

Code review pipeline example:

1. Linter Agent — receives raw code, outputs a structured list of style and formatting issues
2. Security Agent — receives the same raw code, independently outputs a list of vulnerabilities and risk ratings
3. Reviewer Agent — receives raw code plus both lists, synthesises a coherent review with prioritised action items

The Reviewer Agent works better because it has independent expert opinions to synthesise rather than generating everything from scratch.

Pattern 2: Parallel Fan-Out

Dispatch the same input to multiple agents simultaneously. Collect and merge their outputs. This is the pattern for tasks where different specialists should work on the same material independently.

When to use it: competitive analysis (each agent researches one competitor), multi-perspective summarisation (technical, business, legal angles), batch processing where items are independent.

In AACFlow: use the workflow canvas to route one block's output to multiple AI Agent blocks running in parallel. AACFlow's executor handles concurrent execution natively — no custom async code required. A Merge or Response block downstream collects all parallel outputs.

Content production pipeline example:

A single article brief fans out to four agents: one writes the draft, one generates SEO metadata, one creates social media excerpts, one produces an image prompt. All four run simultaneously. A final assembly agent combines the outputs into a ready-to-publish package. Wall-clock time is determined by the slowest agent, not the sum of all agents.

Pattern 3: Debate

Two or more agents argue about a question and iterate toward consensus. Agent A produces an answer. Agent B critiques it. Agent A refines. Repeat for N rounds. A judge agent evaluates the final exchange and produces a verdict.

When to use it: high-stakes decisions where you need to surface hidden assumptions, factual claims that need verification, any task where "is this actually right?" matters more than speed.

In AACFlow: implement as a loop with a counter variable in workflow state. Each iteration passes the running transcript to both agents. The condition block checks round >= N or consensus_reached == true to exit. The judge block sits outside the loop.

This pattern reliably produces better outputs than single-agent reasoning on tasks like legal analysis, medical triage, financial risk assessment, and architectural decision records.

Pattern 4: Hierarchical Orchestrator-Workers

An orchestrator agent receives the task, decomposes it into subtasks, and delegates each subtask to a specialised worker agent. Workers report back. The orchestrator synthesises results and decides whether further delegation is needed.

When to use it: tasks that are inherently tree-structured, where the decomposition itself requires intelligence. Research reports, project plans, codebase audits.

In AACFlow: the Orchestrator is a Mothership Agent block configured with a system prompt that includes decomposition instructions and a tool call to the AACFlow workflow API to trigger worker sub-workflows. Workers are separate AACFlow workflows invoked via the child-workflow block type. Results flow back through the orchestrator's context.

Example: a due diligence report. The orchestrator receives a company name and decomposes the task into financial analysis, technology assessment, team evaluation, market positioning, and legal risk. Each sub-task runs as a separate worker workflow with access to the appropriate data sources. The orchestrator aggregates findings and writes the executive summary.

Choosing the Right Pattern

In practice, production workflows combine patterns. A hierarchical orchestrator fans out subtasks in parallel, and each subtask may be a sequential chain internally. AACFlow's canvas makes these compositions navigable — you can zoom in to see individual agent configurations and zoom out to see the full orchestration topology.

Operationalising Multi-Agent Workflows

Three things determine whether a multi-agent workflow is actually production-ready.

Observability: AACFlow logs each agent's input, output, token count, latency, and cost per execution. In the execution panel, you can step through each agent's turn and inspect the exact prompt and response. This is non-negotiable for debugging unexpected outputs.

Failure isolation: if one agent in a parallel fan-out fails, AACFlow's executor can be configured to continue with partial results or halt the entire workflow. Set this at the block level in the execution settings.

Cost control: multi-agent workflows multiply LLM calls. AACFlow's budget cap feature lets you set a per-execution maximum in USD. If the cap is hit, the workflow pauses and can escalate to a human reviewer. Pair this with a Router block that routes simple subtasks to cheaper models — reserve GPT-4.1 or Claude Opus for the orchestrator and the final synthesis step.

Multi-agent orchestration is not a silver bullet, but it is the right architectural layer for tasks that exceed the reliable capability of a single context window. AACFlow gives you the patterns — the canvas, the executor, the logging, and the cost controls — to apply them without rebuilding infrastructure from scratch.