You've deployed an AI agent into production. It's handling customer inquiries, automating workflows, and delivering real business value. But what happens when it makes a mistake? What if it leaks sensitive customer data in a response or gets stuck in an infinite loop?
Guardrails and evaluations (evals) work together as complementary components of an agent safety and quality framework. Guardrails protect users in real-time. Evaluations establish a quality baseline for agentic systems the start and allow them to maintain the quality bar as data and features change. Understanding the distinction and how to use both evals and guardrails is essential for maintaining AI agent capabilities and the outcomes that are planned/intended for an agent.
What Are Guardrails
Guardrails are logic checks that operate in the request-response path during an agent’s execution. A guardrail filters user input or system output depending on its configuration.
Guardrails make clear-cut decisions: block or allow certain input or output. They don’t measure subjective quality but catch objective failures that should not be exposed to users.
Consider a financial services AI agent that could expose another customer's recent transaction history due to a prompt engineering bug. A PII detection guardrail checks the response, detects account numbers from a different customer, and blocks the response before the user sees it. The conversation is logged, the customer gets a safe fallback message, and engineering is alerted to investigate.
Other failures that guardrails catch include malformed responses like JSON syntax errors, prompt injection attacks where users try to manipulate the agent, token limit violations, explicit or toxic content, and detectable hallucinations that contradict known facts.
What Makes a Good Guardrails System?
A robust guardrails system has several defining characteristics.
- Minimal latency
- High precision, minimizes false positives
- Clear intervention logic (fallback messages, redactions, constrained retries are logged)
- Composable - each guardrail works harmoniously with others without conflicting results
- Observability for every action (can see which guardrails fire most often, which are ineffective, and which need tuning)
What Are Evaluations
Evaluations are systematic measurements of AI system performance, safety, and reliability run asynchronously against test datasets or production traces. Think of them as auditors that examine work after it's complete, identifying patterns, and feeding findings back into improvement loops.
Evaluations run on curated datasets or sampled production traces. Unlike guardrails, evaluations don't sit in the middle of system response or user input. They measure quality, identify issues, and feed data into dashboards, but the original prompt or response has already been delivered to the system or user.
Evals measure subjective quality that requires human judgment by answering questions such as:
- Does the response accurately answer the user's question?
- Is the response in the right voice for your brand?
- Does it make sense and flow well? Is it complete?
- Would a real user find it helpful?
Eval logic can be automated checks (code-driven rules like "response contains at least 3 sentences"), include human-in-the-loop (expert reviewers applying detailed rubrics), utilize LLM-as-a-judge (using a separate language model to score outputs), or hybrid approaches combining any of these multiple methods.
Consider an e-commerce support agent that's failing on a certain type of requests. Rather than just measuring "success rate," a team analyzes an observability log for 50 failed conversation errors and discovers the pattern: all failures involve customers asking about product comparisons. The agent can describe individual products well but can't reason across multiple products simultaneously. This specific insight drives a focused improvement effort that generic metrics would never reveal.
Building an Efficient Evaluation Pipeline
An effective evaluations system requires several key components. The most important isn't automated scoring but systematic error analysis, meaning reviewing actual user conversations, identifying failure patterns, building a taxonomy of failure types, and understanding why failures occurred. Error analysis reveals application-specific failure modes that generic metrics miss.
Rather than using Likert scales that introduce subjective interpretation, effective evaluations use binary scoring: Pass (this response is acceptable for production) or Fail (this response has a problem). Binary scoring forces clearer results, reduces annotation disagreement, and makes success metrics unambiguous.
Reviewers should apply detailed rubrics to subjective qualities through human-in-the-loop (HITL) evaluation. This captures nuances that automated systems often miss: how well does tone match the brand, does it handle emotional context appropriately, is the advice accurate for this customer's situation. HITL evaluation is slower than automation, but a must-have for subjective quality.
Using a separate, strong language model to score evals helps alleviate the burden of detailed eval review by a person. Although, LLM-as-a-judge evaluation can bridge the gap between HITL and total automated review, validation of the LLM judge's accuracy on a subset of evals is required. Code-driven evaluations work well for objective, deterministic success/fail cases like a response contains required fields, response length is within acceptable range, typos or formatting errors, and keyword existence.
What Makes a Good Eval System?
Evals, like guardrails, are specific to an agent’s use case. Good evals prioritize evaluating important features that are essential to an agent’s success. For instance, hallucinations, relevance of context, tone consistency, bias, conciseness, JSON output are some commonly used evaluators.
A few open source eval tools exist that help streamline the configuration of a robust eval system.
- Arize Phoenix
- LangFuse
- Mastra (agent framework with eval/scorer feature out-of-the-box)
How Guardrails and Evaluations Complement Each Other
Guardrails and evaluations operate at different stages and serve different purposes. Guardrails execute in real-time. Evaluations run asynchronously after responses are generated, never blocking user-facing responses. They measure subjective, nuanced quality issues and take minutes to hours.
During agent development, use evaluations to optimize configurations and understand performance. Test different prompts against your evaluation suite, measure accuracy and hallucination rates, identify failure modes before deployment, and iterate on agent design based on evaluation results.
In the pre-deployment stage, guardrails can be designed based on evaluation findings. If evaluations revealed the agent sometimes exposes PII, build a PII detection guardrail. If evaluations showed outputs are malformed, add schema validation. If evaluations identified recurring hallucination patterns, create guardrails to detect and block them.
Once in production, guardrails maintain runtime safety while evaluations monitor quality. Guardrails catch objective failures in real-time, preventing user-facing issues. Evaluations sample production traffic, measuring overall quality and detecting drift. Engineering teams can spot improvement opportunities continuously. New guardrails and evaluation tests can be added as new failure modes are discovered.
Next Steps
An organization deploying AI agents should implement both systems from day one. Test guardrails against evaluation datasets to ensure they work accurately.
Ongoing, use guardrail logs to discover new edge cases. Use evaluation results to prioritize improvements. Add new guardrails and evaluation tests as patterns emerge. Measure the business impact: cost saved by guardrails, quality improved by evaluations.
Guardrails and evaluations form a comprehensive safety and quality framework that keeps AI agents performing well, maintains user trust, reduces risk, and drives continuous improvement. The organizations getting the most value from AI agents treat guardrails and evaluations as symbiotic parts of a single system.
Start with generating evaluations to understand your agent's failure modes. Use those insights to build guardrails for the highest-impact risks. After deploying both, monitor observability logs frequently to understand where improvement need to be made.