Part 8: Multi-Agent Systems

# Part 8: Multi-Agent Systems --- So far, we’ve talked a lot about what makes a single agent act — from tools and RAG to memory and planning. But what if your agentic pipeline needs to:

• Parallelize tasks to speed things up
• Use different agent personas for different parts of a task
• Break up complexity across specialized units, like in a team
That’s where multi-agent systems come in. --- ## Why Use Multi-Agent Systems? Sometimes, a single agent just can’t cut it because the problem demands scale, specialization, or parallel thinking. Examples:
• Generating a marketing strategy that needs market insights, legal review, and creative suggestions.
• Building a compliance assistant that needs to extract information, flag risks, and cross-check policies.
• Automating a sales process where one agent talks to the user, another enriches data, and a third handles follow-ups.
Could you do this with one beefy agent? Maybe. But splitting it into multiple, specialized agents can enable:
• Parallelization → Agents work on parts of a task simultaneously
• Specialization → One agent is great at legalese, another at writing emails
• Tooling independence → Each agent can have its own tools and memory
--- ## Flat vs Hierarchical Agent Coordination All multi-agent systems need some way to coordinate. Two common communication patterns: --- ### 1. Hierarchical Patterns (More Controllable) An orchestrator agent delegates subtasks to others. It sees the full picture and controls the flow. Use when:
• Tasks can be clearly decomposed
• You want tight control
• You have known agent roles (e.g., summarizer, generator, checker)
Think: enterprise workflows, tool suites, parallel pipelines. --- ### 2. Flat Patterns (More Dynamic) Agents talk to each other as peers — no boss. Use when:
• Tasks need creativity or debate
• You want agents to evaluate each other
• There’s no one “correct” answer path
Think: brainstorming, ranking options, multi-view reasoning. --- ## What Nobody Tells You: Multi-Agent Systems Are a Pain On paper, this sounds great. And sure, you can build quick multi-agent prototypes and have fun with them. But for customer/enterprise use cases… it’s painful. Most people read a blog on multi-agent systems and get excited about modularity — > “It’s like microservices!” they say. But AI agents are not microservices. Unlike code, AI models are non-deterministic. They don’t always behave the same way. Adding more agents means:
• More non-determinism (variation across agents, not just within one)
• More memory and state complexity (who knows what, and when?)
• Higher latency and cost
• More coordination bugs and failure points
• More collusion, where agents agree when they shouldn’t (happens more than you think)
Honestly, I could write a book on how painful it is to get multi-agent systems working reliably. --- ## So… Should You Use Them? My personal rule: > In the enterprise, don’t start with multi-agents. Start with one. Let that one agent fail — empirically (via eval metrics) or operationally — before you scale. From my experience, 70%+ of enterprise use cases work just fine with a single well-designed agent — one that uses tools, memory, RAG, and planning. --- ### Multi-agent systems shine when:
• The task is big enough to need parallel execution
• You need clear specialization
• You want creative debate, evaluation, or distributed decision-making
Even then, you need strong design — especially around memory, state, and communication protocols. --- ## Final Word: Problem First, Always This has been our mantra from Day 1: > Don’t build a multi-agent system because it sounds “agentic.” > Build it if — and only if — your problem needs it. The only way to know?
• Have the right metrics
• Test
• Let simpler systems fail first