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

Up Next

In the next part, we’ll talk about real-world agents and how they function.