Hidden Pitfalls of Building Your Own Agent and How to Avoid Them

AI agents have become the modern employee's new digital coworker. They automate tasks, answer questions, and stitch together workflows across tools. But as businesses rush to adopt AI and empower their teams to "build their own AI", many unknowingly create more chaos than clarity. Without structure, guardrails, and governance, teams quickly end up with unproductive agents, overlapping (or incorrect) use cases, inconsistent data outputs, and new cybersecurity risks.

Featured in this Agent Pitfalls Breakdown:

Pitfall #1: Weak Data Governance
Pitfall #2: Agent Sprawl
Pitfall #3: Inadequate Testing
Pitfall #4: Cybersecurity Blind Spots
YouTube Demo: Build Your Own AI Agent w/ M365 Copilot
How Do You Avoid (and Build) Productive and Secure Agents, Then?

Pitfall #1: Weak Data Governance

Moral of the story: Your agent is only as smart as the data you feed it.

Where Data Governance Breaks Down

No Labeling or Security Scoping
If content isn't tagged properly, agents "see" more than they should or miss the actual source of truth. Without labels, the agent pulls outdated or unrelated content because it cannot distinguish authoritative data from noise.
Creators Have No Governance, or Oversight
Employees build agents based solely on what they think should be included. No one monitors what data sources the agent is pointing at or whether they're updated, duplicated, or misconfigured.
Departments Holding Separate or Outdated Documentation
One department updates a handbook, policy, or any internal document that the entire company can access, but another department still has an older version sitting in another bucket. The agent unknowingly will then pull the wrong document, destroying trust with users.

These things, combined with the pace at which data changes (make sure you're updating your agents!) and the erosion of agent trust when answers are frequently wrong, a lack of focus on data governance can lead to many pitfalls beyond just these.

Pitfall #2: Agent Sprawl

Moral of the Story: Everyone builds an agent...and they all the same thing (or have bad data).

One of the biggest problems we see after agent building exercises is an explosion of agents across all departments (we get it, we're excited to have our own dog in the race, too!). Marketing builds one. HR builds one. Accounting builds three. Operations spins up five more. Before long, you have dozens of agents, most of which will overlap, conflict, or barely function.

Where Sprawl Comes From

Multiple Departments Create the Same Agent with Different Data Sources
Each team builds a slightly different “HR Policy Bot” or “Ops FAQ Bot,” creating confusion instead of clarity.
No Centralized Place to Request, Approve, or Catalog Agents
Without a governance layer, there’s no way to know what already exists or what should be consolidated.
Apps You're Already Using Introduce Their Own AI Tools
Teams adopt AI features in their respective tools (BrightGauge, HubSpot, Connectwise, Sharepoint, etc.) and none of them talk to each other. All of them create more work.

Then there's a lack of cross-departmental visibility of who is using what and or for what purpose with little sharing of reporting capabilities. Agent sprawl is confusing, annoying, and frankly unnecessary.

Pitfall #3: Inadequate testing

Moral of the story: Agents go live before they're ready.

In training after training, we see the same patterns: teams publish their agents too quickly. Excitement overrides the process. The result? Broken workflow, inconsistent responses, and confused employees (and ultimately, work slows down to parse through it all).

Where Testing Fails

Speed Prioritized Over Reliability
"We just need it live" mentality leads to more cleanup and later user frustration.
Teams Test Only "Happy Paths"
If the agent can answer one version of the question, teams assume the agent works. But users never ask questions the same way twice.
No Structured Test Script or Success Criteria
Without defined scenarios, you miss the edge cases where the agent behaves unpredictably.
Agents Deployed Before Security and Permission Checks
This is a big one! Teams forget to confirm what data the agent can actually access in production.

And then, of course, as mentioned above, policy files will change, no regression testing occurs, and then answers degrade over time. It's a slippery slope. TEST YOUR AGENTS.

Pitfall #4: Cybersecurity Blind Spots

Moral of the story: AI introduces new risks most teams miss.

AI agents are not "just chatbots." They're powerful automation systems that can retrieve internal data, trigger workflows, and integrate with third-party APIs. Without security oversight, you unintentionally open doors into your environment.

Top Cyber Risks

Over-Permissioned Agents
Agents often get broader access than they need (or should ever have). If compromised, they expose more data than a typical employee account.
Unauthorized Data Exposure Through Poorly Labeled Content
Sensitive documents with no labels become accidentally searchable by the agent.
Shadow AI Development by Departments
Employees create agents without security involvement, approvals, or audit logs.
API Integrations Without Governance
Connecting integrated AI like Copilot to SaaS apps without proper authentication or secret management creates serious attack surfaces.
Lack of Monitoring or Auditing
No one checks what the agent access, what it generated, or where data went.

Demo: Build Your Own Agent with m365 Copilot Studio

How Do You Build Productive, Secure, and REliable Agents?

Governance first, Technology second.

Your business needs:

A unified AI governance model
A catalog of approved agents and use cases
A process to review, update, and retire agents
A data labeling and permissions strategy
Formal testing AND validation before EVERY deployment
Cross-departmental visibility to avoid duplicate work
Minimum-necessary permissions, monitored continuously

When these pieces are missing, the result is always the same: agent sprawl, inconsistent answers, poor adoption, frustrated users, and new security risks.

What's Next?

If you need help or want a place to start, let us know! We can talk you through your options and how to build your agents in real time.

Got more time? Here are a couple AI resources that might help you:

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Hidden Pitfalls of Building Your Own Agent and How to Avoid Them