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Every robust AI system I’ve built – and every fragile one, too – has one thing in common: the foundation is everything. I want to lay out why we always start simple, how you check what’s happening in your agentic system, and the real hazards of leaping into complexity before you’ve truly got the basics right.
Many teams race ahead, layering features and “automatic” cleverness, but then end up firefighting constantly. Trust breaks down and nobody relies on the automation. If you take nothing else away, let it be this: reliable agentic AI is built on pragmatic engineering and discipline, not promises. Here’s the proven approach from real product teams building with Agentica AI and similar architectures.
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1. First, Know When Your System is Alive (or Not)
Before you consider scaling or advanced agents, answer some simple but crucial questions:
- How will you know when something starts? How will you know when something has stopped?
- Where are your health checks, status signals, and observability points?
- Can you see, from the outside, whether things are _really_ working, not just _appearing_ to?
If you skip past this, you risk what Alina Capota describes:
> "You can very quickly get incredibly complicated and you end up jumping around from one area to another, from one problem to another, and you never get that kind of safe, solid feeling that things are working. Because they're not."
(source)
That’s why smart system design means placing detection and monitoring at the heart from the start. As a technical deep-dive, Microsoft Azure’s Agent Factory: Top 5 agent observability best practices for reliable AI is essential reading.
2. Build Isolated, Testable Parts First
Resist the temptation to wire everything together at once. Instead:
- Set up your Master Control Process (MCP) as a self-contained unit.
- Run it until you get clear, repeatable, observable feedback.
- Log every run, every error, every strange behaviour.
Only when your foundation is solid do you start introducing agents. Do it one at a time, each time ensuring full health, response, and behaviour checks before moving on.
Alina Capota’s guidance rings true:
> "Start small and focused : begin with single-responsibility agents; each with one clear goal and narrow scope. Broad prompts decrease accuracy; narrow scopes ensure consistent performance."
(source)
For a highly readable, real-world look at this approach, I also recommend Best Practices for Building Agentic AI Systems: What Actually Works.
3. Define Outcomes First, THEN Add Complexity
The most disciplined teams set boundaries up front:
- Define what each agent should do (and what it shouldn’t).
- Specify measurable outcomes, not vague targets.
- Hold off on integration until each new part proves itself in isolation.
As Capota says:
> "Align agent goals and measurable outcomes : define clear objectives, performance metrics, and success criteria before design begins. Agents should operate within measurable boundaries."
(source)
Never pile on features before the foundations are rock solid. Check out Agentic AI Architecture: Types, Components & Best Practices for further architectural wisdom.
4. Essential Takeaways for Building Solid Systems
This is the approach that leads to agentic AI systems which scale and survive in the real world, not just in demos:
- Start with the minimal working part, instrumented for detection and reliability.
- Test each agent-to-agent (A2A) interaction on its own before scaling up.
- Only layer in new features or complexity when the base is truly solid – not “sort of works”.
- Be clear on _how_ you know your system is alive, stopped, healthy, or broken.
Above all: reject “mostly working”. Capota is blunt:
> "We don’t allow kind of working. We need solid and we need reliable."
(source)
That is how you achieve trust and long-term automation value.
