What Can Go Wrong? Understanding Risk & Failure Modes in Agentic AI

“It didn’t crash. It didn’t throw an error. But it still got everything wrong.”

When AI fails, it doesn’t always look like a bug.
There’s no red flag, no 500 error, no broken link.
Just a soft, quiet failure  and your test plan didn’t see it coming.

Why This Post Matters

Most test managers know how to spot failures:

A button doesn’t work. A value’s incorrect. A workflow breaks.

But with agentic AI systems, failure is often invisible to the system and painfully obvious to the user.

Agentic AI doesn’t break like software.

It fails like a human: subtly, logically… and sometimes confidently wrong.

This post will help you spot those failures before your customers, auditors, or executives do.

The New Risk Landscape

Agentic systems introduce new categories of risk that traditional test planning doesn’t account for. Let’s break them down:

1. Hallucinations

The system invents facts, steps, or content that wasn’t in the source material.

🧪 Example:
Your AI assistant recommends a product feature that doesn’t exist because it inferred something from vague marketing copy.

⚠️ Why It Slips Through Testing:
The response looks well-formed and confident. It’s syntactically perfect… but semantically false.

2. Goal Misalignment

The system is technically correct but pursues the wrong outcome.

🧪 Example:
An AI agent handling refunds chooses the option that minimizes cost to the company not the one that matches the refund policy.

⚠️ Why It Slips Through Testing:
Your test case says, “Issue refund on policy match.” The agent’s reasoning says, “What’s the cheapest way to satisfy the customer?”

3. Tool Overuse or Misuse

The AI chooses to use a tool when it shouldn’t or repeatedly relies on the wrong one.

🧪 Example:
A multi-agent system keeps triggering a database query loop instead of summarizing cached results.

⚠️ Why It Slips Through Testing:
Each step appears valid in isolation but the pattern reveals inefficiency, latency, or resource exhaustion.

4. Memory Drift

The system recalls information incorrectly or relies on outdated context.

🧪 Example:
The AI insists the user lives in New York because it remembered an old trip itinerary as their home address.

⚠️ Why It Slips Through Testing:
Memory isn’t static. Your test data may pass once… but evolve or degrade across sessions.

5. Escalation Failure

The system doesn’t know when to stop or hand off to a human.

🧪 Example:
An AI support bot keeps “trying” to solve a billing issue instead of escalating after 3 failed attempts.

⚠️ Why It Slips Through Testing:
You tested happy paths and a few fallbacks… but not the moment it should’ve said, “I don’t know.”

6. Safety and Ethical Violations

The AI provides answers or makes decisions that violate safety protocols, compliance rules, or ethical norms.

🧪 Example:
A wellness chatbot gives medical advice instead of directing the user to a qualified professional.

⚠️ Why It Slips Through Testing:
You didn’t test for edge-case prompts or ambiguous intent and the AI filled the gap with confidence.

These Aren’t Edge Cases - They’re Normal Now

Here’s the dangerous thing: these failure modes often look “correct” to automated checks and simple scripts.

You won’t catch them by asking:

• “Did the output match the expected value?”
• “Did the function return successfully?”

You need to ask:

• “Was the behavior appropriate?”
• “Was the goal correctly interpreted?”
• “Was the reasoning path valid?”
• “Would a human have made the same call?"

Mapping Old QA Concepts to New Risks

To help your team navigate this shift, here’s a mapping:

Simple truth: You already know how to manage risk.
You just need a new lens for observing where it now hides.

What Should You Do With This?

Here’s a new way to think about your test planning:

✅ When writing a test case, ask:

• What’s the intent this system should pursue?
• What are unsafe, inefficient, or unintended behaviors it might invent?

✅ When analyzing test results, ask:

• Does the output make sense in context?
• Is it factually grounded and goal-aligned?

✅ When designing test coverage, ask:

• Have I checked not just the answers, but the reasoning?

Final Thought: You Can’t Prevent What You Can’t See

Agentic systems fail like interns: they sound confident, look productive, and occasionally do something wildly off-base.

Your job is no longer just to check whether “the flow works.”
Your job is to understand how and where the agent can go off track.

The systems aren’t breaking.
They’re doing what they were trained to do just not what you expected.

Coming Next:

Blog 3 – “Rethinking Coverage: What to Measure When You’re Not Testing a Flow”