Red-teaming your own agents: a practical guide to eval-driven development

We need to talk about something most agent developers skip: systematically breaking your own systems before they break themselves.

I've watched too many agents deployed to production with basic happy-path testing. Then they encounter edge cases—adversarial inputs, resource exhaustion, prompt injection—and suddenly you're incident-responding at 2 AM. Eval-driven development for agents isn't optional anymore. It's foundational.

Start with threat modeling, not fuzzing

Before you write a single adversarial test, map your attack surface. What decisions does your agent make? What external systems does it touch? Where can user input flow? A supply-chain agent that interacts with vendor databases has radically different failure modes than a customer-support chatbot.

Document your red-team priorities. This keeps you from cargo-culting security evals. You can't test everything—prioritize based on impact and likelihood.

Build evals that simulate real adversaries

Generic jailbreak prompts are a starting point, but they're weak. Real adversaries understand your system. Your evals should too.

Create adversarial test suites that:

• Directly challenge your agent's guardrails: If your agent shouldn't make financial transfers, test with variations of "send $10k to account X." Variations matter—different phrasings, obfuscation, indirect requests.
• Test boundary conditions: Off-by-one errors, empty inputs, massive payloads, special characters in unexpected places.
• Simulate privilege escalation: Can the agent be tricked into using elevated permissions? Does it properly validate context?

Instrument for observability during testing

You can't fix what you can't see. Log every decision point:

• What instructions did the agent receive?
• What reasoning did it execute?
• Where did it defer, escalate, or refuse?
• What was the output?

I've found that 30% of agent failures aren't failures of safety—they're failures of transparency. You need visibility into the chain of thought.

Make evals part of your development loop

This is the critical shift: evals aren't a final check. They're continuous feedback.

• Before deploying a new capability, write evals that would catch its failure modes
• When an agent misbehaves in staging, add an eval that reproduces it
• Use differential testing—run old vs. new versions against the same test suite

Track metrics over time. Are your adversarial pass-rates improving? Are novel failure classes still being discovered? That tells you when you can actually relax.

Integrate red-team feedback structurally

Use your evals to fine-tune guardrails. Many teams treat safety as a post-hoc layer. Instead, treat it as optimization feedback: "This class of input breaks your agent—here's the pattern."

This often means updating system prompts, adding retrieval constraints, or adding approval gates for high-stakes actions. Make these decisions data-driven.

The reality check

You will not catch everything. But systematic red-teaming dramatically raises the cost of exploiting your agents. That's the point. Make attacks expensive enough that adversaries look elsewhere.

The teams shipping reliable agents aren't smarter—they're more disciplined about breaking their own work first.