Multi-agent systems are exciting to build, and they’re also easy to get wrong.
I’ve noticed a recurring pattern when developers start building with agents. The first agent is clear and focused. The second adds value. The third feels clever. By the fourth or fifth, the system still “works”, but no one knows why. Or what would break if something changed, this is common.
That’s the moment when complexity starts creeping in and the problem isn’t intelligence, it is the structure.
Agents Are Software Components First
Agents may reason, decide, and adapt. But they are still software components.
They evolve over time. They depend on inputs. They interact with components built by different people.
When an agent misbehaves, we try to “fix” that agent by either adding more context or by updating the prompt or by changing the underlying large language model.
But most failures in agentic systems are systemic, not local. E.g. an agent that knows too much, decides too much is responding to a lack of structure around it.
In healthy systems, components don’t succeed by being exceptional but they succeed by knowing their place.
At a glance, a well-designed agentic system feels calm, not because it’s simple – but because responsibilities are clear.
The Hidden Cost of “Helpful” Agents
One of the most common anti-patterns in multi-agent systems is the over-helpful agent. It starts with good intent. It ends with confusion. example below:
- This agent already has the context, let it decide.
- It can handle this edge case too.
- We’ll refactor later.
and over time, such agents become harder to replace, harder to reason about and harder to trust. The system still works., but it works only because agents compensate for each other. This is not robustness. It is social coordination disguised as architecture.
SOLID as a Mindset for Agent Design
This is where familiar software design principles become relevant again. SOLID principles were never about classes or objects, but they were about how responsibility is distributed.
That makes them relevant for agentic systems – where responsibility is implicit and loosely defined. Think of SOLID not as rules, but as shared norms. When everyone follows the same norms, coordination emerges naturally.
Single Responsibility: One Agent, One Role
In well-functioning systems, roles are clear enough that others don’t need to guess.
An agent with single responsibility is easier to test, easier to replace and easier to trust.
Open / Close: Growth Without Disturbance
Healthy systems grow by adding new capabilities, not by rewriting existing ones.
In agentic implementations, new behavior comes from new agents, while existing agents remains unchanged and stable.
Remember that, when adding a new agent requires modifying several others, the system no longer follows shared rules, instead, agents negotiate with each other.
Liskov Substitution: Trust Through Consistency
A role matters more than who performs it and Liskov Substitution focuses on the fact that the role remains stable even when implementations change.
An agent represents a responsibility, not a personality. Over time, you may replace an agent with newer version with complex changes, bring faster or cheaper alternative of implementation, so If doing these changes, system implementation should remain stable as it never relied on the personality but it depended on the responsibility (as known as contracts).
Interface Segregation: Less Context, Better Decisions
More context does not make an agent smarter.
Agents perform best when they receive only what their role requires. Extra information doesn’t make them wiser – it makes their behavior harder to predict and also makes them costly.
Dependency Inversion: Depend on Roles, Not Personalities
Robust systems depend on capabilities, not specific agents.
When agents rely on how others think internally, the system becomes fragile. When they rely on what others promise to deliver, the system becomes resilient. This is how independent contributors coexist without constant coordination.
Shared Principles, Independent Agents
What makes multi-agent systems robust isn’t central control or clever coordination but it’s something more subtle.
Each agent is built independently, by different people, at different times – yet the system behaves coherently. That coherence emerges when everyone designs with the same principles in mind. When responsibilities are clear, agents don’t need to constantly negotiate or compensate for each other.
A Shift in How We Build Agentic Systems
Building agentic systems requires a mindset shift:
- Design agents as long-lived components, not experiments
- Optimize for change, not demos
- Prefer clarity over cleverness
Well-run systems don’t depend on exceptional individuals, but they depend on shared discipline.
Old Principles, New Systems
We didn’t abandon software design principles when systems became distributed and we shouldn’t abandon them now that systems have agents.
Agents are new, the need for responsibility, boundaries, and clarity is not.
The most resilient agentic systems will be built by teams who combine AI capability with engineering discipline – and who remember that structure is what allows intelligence to matter.
Closing Thoughts: A Simple Check Before You Build an Agent
Before adding a new agent to a multi-agent system, pause for a moment and ask yourself a few simple questions:
- What role does this agent exist to serve – and only that role?
- Could this role be fulfilled by a different agent without breaking the system?
- Am I adding capability by introducing this agent, or am I compensating for missing structure elsewhere?
- Does this agent depend on clear roles, or on assumptions about other agents?
- If many agents were built this way, would the system feel calmer or more fragile?
These questions don’t make agents smarter, but they make systems more maintainable.