At Axirian, we jump into AI systems every week. Some are tidy little playgrounds. Others look like someone tried to build a spaceship using leftover parts from five different IKEA sets. No matter the setup, we almost always hear the same line during the first meeting:

“The model seems better this week.”

Whenever someone says that, I know we’re about to step into an environment where things are operating on optimism instead of observability. Smart engineers, good intentions, and a whole lot of undocumented magic holding everything together.

After conducting dozens of these reviews, seven issues show up so consistently that they may as well be industry standards. Here’s what we find inside almost every AI architecture, regardless of company size or maturity.

1. Prompts are everywhere except in version control

Prompts live in Slack messages, personal notebooks, Airtable scripts, random folders, or my personal favorite, a file named “final_v23_really_final.”
Nothing is versioned. Nothing is tracked. And absolutely nothing is benchmarked.

When nobody knows which prompt produced which result, you’re not running an AI system. You’re running an improv show.

2. No visibility into what the agent actually thought

Most teams track performance metrics like response time, cost, or CPU usage.
That’s great for traditional software, but AI systems break in more creative ways.

Ask a team what the agent’s plan was, where it changed course, or whether it hallucinated mid-workflow, and the answers get vague fast. Without trajectory logs or reasoning traces, debugging an agent feels like trying to interrogate a raccoon. It just looks at you confidently as if nothing weird happened.

3. The workflow map looks like a conspiracy board

A lot of companies unintentionally create automation sprawl. Tools get connected “for now,” temporary scripts turn into permanent fixtures, and suddenly no one remembers why the CRM is talking to the warehouse system through Zapier and a Python script created by someone who left two years ago.

AI doesn’t simplify this. It makes the chaos louder. We spend a good amount of time helping teams untangle the spaghetti and rebuild clean, intentional paths that are actually maintainable.

4. The dream of multi-agent systems meets the reality of missing basics

Many executives want impressive multi-agent orchestration. Meanwhile, the single agent they already have is allowed to do whatever it wants with no guardrails, error handling, or policy logic.

We frequently see agents calling tools they shouldn’t, or even calling each other in loops that nobody planned. It’s like watching unsupervised toddlers with walkie-talkies.

Before you scale to multiple agents, you need one agent that behaves predictably on its worst day, not just its best.

5. The blast radius is completely undefined

AI agents often have more access than the humans who operate them. That’s a problem.

We find agents with direct write access to production systems, agents capable of updating customer records without validation, and agents making decisions nobody even knew they could make.

If you don’t intentionally define what an agent can and cannot touch, the system will make that decision for you. And it usually picks the wrong thing.

6. Testing stops at “does the chat response look reasonable”

Traditional QA falls apart the moment you introduce autonomous behaviors.
Consistency disappears. Edge cases multiply. Reasoning becomes variable.

Most teams have no mechanism to test how an agent behaves across different scenarios, unexpected tool failures, model drift, or hallucinations. At Axirian, we use structured frameworks like LangGraph tests, along with evaluation tools like LangSmith and TruLens, and more traditional observability stacks such as Prometheus and Grafana.

If you’re not testing the reasoning chain itself, you’re missing the part that actually breaks.

7. Hidden prompts lurking where nobody expects them

Shadow prompts are real, and we find them constantly. They hide inside CI pipelines, Zapier steps, Airtable automations, GitHub Actions, Jupyter notebooks, or old scripts that everyone forgot about.

These prompts quietly influence business logic without review or governance. It’s the AI equivalent of discovering that your production database is being updated by a forgotten cron job named “test_script.py.”

Why an architecture review matters

Most teams don’t bring us in because they want fancy diagrams or another stack of recommendations. They bring us in because AI systems feel unpredictable, and unpredictability is expensive.

A proper review brings everything back under control:
clear observability, clean workflows, defined responsibilities, governed prompts, and agents that behave the way you expect them to. It replaces the weekly “the model seems better now” guesswork with actual understanding.

And once the foundation is solid, then you can build the ambitious things: reliable agents, smart automations, and multi-agent orchestration that doesn’t collapse under pressure.

If any of these issues sound familiar, you’re not alone. We see them everywhere. Fixing them just happens to be exactly what we’re built for.