Building a Multi-Agent AI System: When One AI Is Not Enough
Single AI agents work well for bounded tasks. For complex, multi-step business processes, multi-agent systems — where specialised AI agents collaborate — deliver results that no single agent can achieve alone.
The Core Concept
A multi-agent system is a collection of individual AI agents, each specialised for a specific sub-task, that work together to complete a larger goal. One agent researches. Another writes. Another reviews. Another publishes. Each agent does one thing well, and passes its output to the next agent in the pipeline.
The analogy is a specialist team versus a generalist employee. A single AI agent trying to do all tasks simultaneously makes trade-offs and produces mediocre output at each step. Specialised agents, like specialised team members, produce better output at each step because their context, instructions, and evaluation criteria are optimised for exactly one job.
Choose the Right Architecture for Your Use Case
Pipeline (Sequential)
Agent A produces output → Agent B processes A’s output → Agent C processes B’s output → final result. Best for: content production workflows (research → draft → edit → optimise), data processing pipelines (extract → transform → validate → load), and document workflows (read → classify → extract → route). Each agent gets the previous agent’s complete output as input.
Orchestrator + Workers (Hub and Spoke)
One orchestrator agent receives the overall goal, breaks it into sub-tasks, assigns each to a specialised worker agent, collects their outputs, and synthesises the final result. Best for: complex research tasks, project planning, and any task where the sub-tasks are variable and cannot be hardcoded in advance. The orchestrator decides what to do; workers execute.
Review Loop (Generate + Critique)
Agent A generates output → Agent B critiques it against specific criteria → if critique identifies issues, Agent A regenerates with the critique as additional context → loop continues until Agent B approves. Best for: high-stakes content (legal documents, financial analysis, medical information), code generation (generate → test → fix → retest), and any output where quality consistency is critical.
A Multi-Agent Content Production System
This system produces a complete, SEO-optimised blog post from a keyword — using 5 specialised agents in sequence.
Agent 1: Research Agent
Input: target keyword. Tools: web search. Task: find the top 10 ranking articles for this keyword, identify the key topics they cover, find statistical data and expert quotes, and identify gaps — what do these articles NOT cover well? Output: a structured research brief with sources.
Agent 2: Outline Agent
Input: research brief + keyword + brand guidelines. Task: create a detailed article outline that covers the research findings, addresses the content gaps identified, incorporates the target keyword and semantic keywords naturally, and follows the brand’s content structure. Output: section headings, sub-points, and notes on what each section should cover.
Agent 3: Writing Agent
Input: outline + research brief + brand voice guidelines. Task: write the full article following the outline exactly, incorporating the research, matching the brand voice, and maintaining a consistent argument throughout. Output: full draft article (1,500–2,500 words).
Agent 4: SEO Review Agent
Input: full draft + target keyword + SEO guidelines. Task: evaluate keyword density, heading structure, internal link opportunities, meta description draft, and readability. Identify any SEO issues and suggest specific fixes. Output: SEO audit with specific recommendations.
Agent 5: Editor Agent
Input: full draft + SEO audit. Task: apply the SEO recommendations, improve sentence variety, fix any factual claims that need verification flags, tighten the introduction and conclusion, and produce the final polished version. Output: publish-ready article.
Practical Implementation
Make.com for sequential pipelines
For pipeline-pattern multi-agent systems, Make.com is the orchestration layer. Each AI module in the scenario is an agent. The output of one module is mapped to the input of the next. Error handling, retries, and logging are built into the Make.com infrastructure. No custom code required.
LangGraph for complex orchestration
For orchestrator-worker and review-loop patterns that require dynamic task assignment, LangGraph (Python) provides a graph-based workflow framework designed for multi-agent systems. Requires developer capability but enables significantly more sophisticated agent coordination than Make.com.
OpenAI Assistants API with handoffs
OpenAI’s Assistants API supports agent handoffs — one assistant completing its task and passing control to another assistant. Suitable for customer-facing multi-agent systems where different specialists handle different parts of a conversation.
The Decision Framework
| Situation | Single Agent | Multi-Agent |
|---|---|---|
| Task complexity | Single, well-defined task | Multiple distinct sub-tasks requiring different expertise |
| Output quality requirement | Good enough for internal use | High-stakes output requiring multiple review passes |
| Context length | Fits in single context window | Exceeds context window or benefits from fresh context per step |
| Development resources | Minimal — one prompt to write | Higher — architecture, coordination, error handling |
| Output volume | Low to medium | High — parallelisation across worker agents saves time |
| Debugging need | Simple — one agent’s output to review | Complex — must trace through multiple agents |
📌 Start with a single agent for every use case. Only move to multi-agent when a single agent demonstrably cannot produce the required output quality — not as a premature optimisation. Most business use cases are solved well by a single agent with an excellent system prompt.
Want a Multi-Agent AI System Built for Your Business Process?
SA Solutions designs and builds multi-agent AI systems for complex business workflows — from content production pipelines through research automation and document processing.
