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Building Robust Agentic AI: Why Simplicity and Observability Matter More Than Cleverness

Tony Wood |

Because when we're working with Agentic AI, one of the best methods is to start working with good data and system design. Think about how, well, aroused that your system is gonna run on. How will you know when something starts? How are we know when there's something has stopped? How you know when things work and when they don't work, how you know when they're broken when they're not broken? That's your starting point.

From years of working with both startups and large product teams, I've noticed a pattern: There's a temptation to leap straight into the “magic” of AI-powered automation, skipping the long, sometimes dull business of building a solid foundation. Teams get lost in complexity—chasing features, orchestration, or “agentic” autonomy—only to discover their systems are sort-of-alright, but never totally reliable. And that is a recipe for disaster at scale.

What follows is my take on how to approach agentic AI systems so you can avoid “kind of working”, and instead build something you’ll trust over the long run.

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Start With Reliable Signals and Good Data

The first discipline in agentic system design is not a fancy algorithm or clever use of an LLM. It is observability. Can you tell, in real time, when any part of your system starts, stops, succeeds, or fails? Can you see what each agent is doing, why it did it, and when? Get that right, and problems become trivial. Get it wrong, and you’ll spend hours chasing ghosts.

The Exabeam Team captures this perfectly:
> “Signals such as when an agent starts, stops, succeeds, or fails should be explicitly surfaced, logged, and available to orchestrators. Reliable feedback loops and error handling form the backbone of scalable agentic systems.”
> Agentic AI Architecture: Types, Components & Best Practices

So, before you add complexity, insist on concrete answers:

  • Can you tell if the orchestrator (sometimes called the MCP: Master Control Program/Process) is running?
  • When and how does it start and stop?
  • Do agents log their results, or do you hope for the best?
  • Is error handling explicit, or is it a black hole?

If these sound dull, tough. These basics are the difference between getting woken up at 2am, or sleeping well while your system handles the unexpected.

If you want a blueprint for these steps, the Exabeam guide is an excellent place to start:
Agentic AI Architecture: Types, Components & Best Practices

Build the Minimum Viable Orchestrator—And Keep It Simple

Next up: Build your central “brain” (the orchestrator, or MCP), but resist all urges to make it clever. Give it one job: Run agents, record signals, and surface errors. If you can’t see what’s happening, you haven’t finished step one.

As McKinsey’s QuantumBlack advisors have written:
> “The most successful agentic-AI implementations we have seen are grounded in proven systems engineering principles, with tightly defined modular components and robust interfaces between them.”
> Seizing the agentic AI advantage

The principle is modularity. Each part should be testable alone, and only connected when you can prove it works.

Agents In Isolation: Test Each, Don’t Trust Each

This is where most teams get impatient. They add five agents, throw them in, and hope. Instead: Write, run, and verify one agent at a time. Give it a spec—then prove it does what it says on the tin.

If it falls over, you’re glad you caught it now, not in production.

Do Not Tolerate “Kind of Working”

Here’s the thing… It’s easy to end up with systems that feel all right but are endlessly shaky—cycling through weird edge cases and low-level flakiness. I’ve seen this burn teams repeatedly. Trust me, “kind of working” isn’t good enough for agentics. As I often say,
> Because 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. They're kind of working. And we don't allow kind of working.

Curtis Northcutt sums up the risk of skipping reliability layers eloquently:
> "Without a formal reliability layer, agent-based architectures can quickly devolve into opaque, semi-working systems where intermittent failures go undetected or untriaged. Observability and rigorous validation are essential."
> The Emerging Reliability Layer in the Modern AI Agent Stack

The True Role of Agentic AI: Automate the Uncertain, Not the Obvious

Think of agentics as your system’s explorer—it shines where you can’t write simple rules or deterministic logic. Automate the unknown, the ambiguous, the edge cases. But never let AI be an excuse for skipping engineering discipline.

The ByteByteGo team captures this distinction well:
> "Agentic AI shines most when automating uncertainty: tasks and decisions with inconsistent structure, ambiguous context, or external variables that deterministic systems struggled to handle. Modern workflow patterns make it possible to safely automate the previously un-automatable."
> Top AI Agentic Workflow Patterns

So after your system is observable, reliable, and modular—_then_ bring on the semi-structured, semi-predictable, and semi-known. Use AI to amplify what works, not to patch what’s broken.

Disciplined Engineering > Automation Theatre

It’s hard to be patient. “Moving fast and breaking things” is tempting, especially for smart engineers. But the cost of jumping straight into orchestration and complexity is brutal downtime later. As the OneReach.ai team advises:

> "Many enterprises leap straight into complex AI-agent orchestration without first ensuring that base components are reliable in isolation. Iterative rollout—first validating MCPs or single-agent flows, then layering more—is key to sustainable scale."
> Best Practices for AI Agent Implementations: Enterprise Guide 2026

Get the signals and the basics right, then scale intentionally. There are no shortcuts. There never will be.

Final Reflections and Next Steps

The promise of agentic AI isn’t magic—it’s leverage. Reliable, observable, modular systems give you confidence to automate boldly at the uncertain edges, where rules and tradition have failed. But there’s no backup or alternative to good solid engineering.

Here’s your checklist:

  • Plain signals for every component
  • A simple, testable control layer (MCP/orchestrator)
  • Sound, isolated agent logic before composition
  • Observability everywhere
  • AI for the genuinely hard/unknown bits, never for patching up systemic failings

If in doubt, keep things simple and visible. The most expensive part of complex systems is everything “kind of working” but not really working. Ruthless simplicity now saves pain, confusion, and lost nights later.

If you want further reading and case studies, start here:

If you do nothing else, refuse to accept “kind of working” in your own systems. Simplicity first, then scale. Get in touch if you want to talk through practical approaches or compare scars from the field.

 

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