AI Agents Are Becoming Your Noisiest “Colleagues”. Here’s How to Architect Them Without Losing Control

Not long ago, “adding AI” to a project meant bolting a chatbot onto the product and calling it a day.

Now I’m seeing something very different in client conversations:

AI isn’t just answering questions anymore — it’s starting to do things.

• It opens tickets in Jira.

• It updates records in CRM.

• It tweaks promo campaigns in a marketing system.

• It runs scripts against production (yes, this is already happening).

In other words, AI agents are turning into a new kind of power user in our architecture. And like any power user, they can create a lot of value… or a lot of damage.

This article is a practical walkthrough of how to think about these agents from the very beginning of a project — from the “we have an idea” stage to running them safely in production.

Start with the uncomfortable question: 

What if this thing goes rogue?

When someone says “Let’s build an AI copilot for our customers / support team / finance”, the easiest path is to:

1. Spin up a model,

2. Give it access to your APIs,

3. Ship a demo.

The problem is, that’s exactly how you end up with an agent that one day:

• refunds the wrong customers,

• bulk-updates product prices,

• or sends a very confidential report to the wrong person.

So at the idea / inception stage, I’d start with three very boring but very important questions:

1. Which workflow are we trying to change?

   Not “we want AI”. Something like:

   
   

   • “Shorten first-response time in support by 30%”

   • “Automate 50% of level-1 IT requests”

   • “Cut time-to-prepare financial reports from 5 days to 2”

   
   

2. Where is the agent allowed to read and where is it allowed to write?

   It’s amazing how many projects never map this clearly. A simple table like:

System	Read?	Write?	Risk level
Ticketing	Yes	Yes	Medium
Payments	Yes	No	High
Analytics DB	Yes	No	Medium

will save you from a lot of awkward incidents later.

3. How will we know it’s successful (and safe)?

   
   

   • Business metrics: fewer touchpoints, shorter cycle times, better CSAT (Customer Satisfaction Score).

   • Safety metrics: how many risky actions were blocked, how often humans overruled the agent.

If you can’t answer these three without hand-waving, you’re not ready for the architecture discussion yet — you’re still in idea shaping.

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Think in building blocks, not in “magic AI”

Once the idea is a bit more grounded, I like to draw a very simple picture on a whiteboard. It usually ends up with the same four building blocks:

1. The AI “front door” (gateway / agent hub)

   This is where all AI agents come to do their work. It:

   
   

   • knows who the agent is,

   • knows what it’s allowed to do,

   • routes its requests to the right tools and APIs,

   • and logs everything.

   
   

   It can be implemented with API Management + some custom services, or with an “agent platform”. The point is: AI doesn’t call your core systems directly.

   
   

2. Knowledge layer (so the agent isn’t just hallucinating)

   Agents need context: product documentation, policies, tickets, contracts, logs. That typically means:

   
   

   • ingesting and indexing documents,

   • using RAG (retrieval-augmented generation),

   • and respecting permissions (no, support agent A shouldn’t see finance docs B).

   
   

3. Tools & connectors (where the real work happens)

   These are explicit operations like:

   
   

   • CreateSupportTicket

   • IssuePartialRefund

   • ScheduleCallback

   • UpdateCustomerAddress

   
   

   Each tool should have validation, logging, and ideally a rollback story. The model chooses which tool to use, but the business logic lives in your services, not inside the prompt.

   
   

4. Governance & observability

   This is the part everyone promises to “add later” and then never does:

   
   

   • per-agent and per-tenant limits,

   • approvals for high-risk actions,

   • dashboards with “what did my agents do today, and why?”

Treat your own agents as if they were strangers

Here’s a mental model that works surprisingly well:

Treat your own AI agents as if they were external users you don’t fully trust.

Concretely, that means:

• Least privilege by default

  Each agent gets only the APIs and scopes it needs. Not “read/write everything, because it’s internal”.

• Guardrails around money and bulk changes

  
  

  • Anything involving payments, discounts, or data exports should have an approval step.

  • Bulk operations (e.g., “update 5,000 records”) should be heavily rate-limited or require a human.

  
  

• Kill switch

  There should be a one-click way to stop an agent if it starts misbehaving (for whatever reason — bug, prompt injection, compromised key).

• Paper trail

  Every action should answer:

  
  

  • Which agent did it?

  • With which tool?

  • Based on what context?

  • Who approved it (if anyone)?

  
  

Architecturally, this often means combining:

• an API gateway,

• a policy engine (e.g., OPA-style rules),

• and your logging/monitoring stack (App Insights, OpenTelemetry, etc.).

Accuracy & hallucinations: “Sorry” doesn’t help when you’ve already broken something

An agent giving a slightly off answer in chat is one thing. An agent issuing refunds or changing prices based on hallucinated info is another level of pain.

A few practical tricks here:

• Structure the outputs

  Ask the model to produce JSON following a schema your service validates, instead of free text. If the JSON doesn’t pass validation, you don’t act on it.

• Keep critical logic outside the model

  Let the model decide which tool to use, but not the exact financial rules, tax logic, or inventory calculations.

• Test agents like you’d test a human colleague on probation

  Give them realistic tasks and see what they do.

  
  

  • “You are a support agent. Here’s a frustrated customer. What do you propose we do?”

  • “You are a billing assistant. This invoice looks off; walk me through your reasoning.”

  
  

Use those scenarios as repeatable evaluation sets before going to production.

A realistic rollout path (that doesn’t terrify Ops and Security)

If you’re wondering how to introduce agents without stressing everyone out, this phased approach works well:

1. Shadow mode

   
   

   • The agent suggests actions.

   • Humans still do everything.

   • You compare: “What would we have done vs. what did the agent suggest?”

   
   

2. Copilot mode

   
   

   • The agent proposes an action in the UI.

   • The human clicks Approve/Reject.

   • You collect feedback and adjust prompts, tools, and policies.

   
   

3. Auto-execution for low-risk actions

   
   

   • Very small refunds, simple ticket updates, internal-only changes.

   • Everything logged and visible on dashboards.

   • Easy way to disable auto-mode if something looks off.

   
   

4. Gradual expansion

   
   

   • As confidence grows (and your monitoring matures), you move more scenarios from copilot to auto.

Each of these steps is as much an organizational change as it is a technical one. People need to trust the system — not just the model.

If you want to start this week

Here’s a “first week” checklist I’d be comfortable using with any client:

• Pick one narrow process (e.g., “triage incoming support emails”).

• Draw a simple map of which systems the agent needs to touch.

• Decide:

  
  

  • what it can read,

  • what it can suggest,

  • and what it can actually change.

  
  

• Put a basic “AI front door” in place: even if it’s a single service that proxies calls to your internal APIs, don’t let the model talk directly to everything.

• Ship a tiny shadow-mode PoC and start collecting examples of good and bad suggestions.

From there, you can grow the architecture. But you’ll be growing something that’s grounded in a real workflow, not in hype.