In an era where generative AI is reshaping industries, the promise of powerful, autonomous agents comes with a critical challenge: trust. For technical leaders and developers, the “black box” nature of many AI systems presents a significant hurdle. A single error, hallucination, or compliance breach can erode customer trust and introduce serious business risks.
At Pega, we address this challenge head-on by building predictable, workflow-driven AI agents. However, even with a safety-first design, a robust testing and monitoring strategy is non-negotiable. We will explore why this is crucial, how the testing paradigm is shifting, and how Pega’s architecture integrates with modern evaluation frameworks like DeepEval to deliver objective proof of an agent’s reliability.
Why Testing, Monitoring, and Safety Are Paramount
Clients and developers share a healthy fear of AI’s unpredictability. While we build agents to perform tasks, the non-deterministic nature of the underlying Large Language Models (LLMs) means their behavior can change in unexpected ways. Code freezes no longer guarantee static behavior. An LLM provider might update a model, a sophisticated user could attempt a “jailbreak” to bypass guardrails, or the agent’s responses could drift over time.
This new risk profile raises critical questions that a modern testing strategy must answer:
- Reliability: How do we detect and ensure our agent provides consistent, accurate responses?
- Stability: How do we identify and measure AI drift or model degradation over time?
- Safety: How can we proactively prevent and detect jailbreaks, misuse, or the generation of biased, toxic, or off-brand content?
- Performance: How do we monitor system latency and ensure agents are completing tasks efficiently?
- Adaptability: How do we manage model version changes to ensure our business workflows don’t silently break?
Without empirical answers to these questions, deploying AI agents into production is a leap of faith. We need to move from subjective trust to objective proof.
The New Paradigm: How AI Testing Works
Traditional software testing, built for a deterministic world, is no longer sufficient. The methodologies that have served us for decades must be reimagined for AI-centric applications.
| The Old Way | The New Paradigm |
|---|---|
| Exclusively Binary Pass/Fail Tests rely on rigid logic and hardcoded assertions, where an exact match determines the outcome. |
Probabilistic & Deterministic Evaluation Frameworks like LLM-as-a-Judge are used to score nuanced concepts such as contextual relevance, role adherence, and conversational quality, alongside deterministic checks. |
| Test in Isolation Components are tested in silos by mocking external services, databases, and APIs. |
Live Orchestration Testing AI agents orchestrate complex backends (e.g., RAG, APIs, Case Automation). Testing must validate this entire live orchestration, not just isolated functions. |
| Static Deployments Testing largely concludes once the code passes a pre-deployment gate. |
Continuous Monitoring Scheduled and ongoing evaluations are essential to detect drift, hallucinations, and performance degradation in a live environment. |
| Edge Security Security is focused on creating an impenetrable wall of static, deterministic rules at the perimeter. |
Continuous Guardrails Dynamic, adversarial testing is required to continuously monitor for prompt injections, off-topic queries, and the avoidance of toxic or biased outputs. |
Pega and LLM-as-a-Judge: A Two-Pronged Evaluation
Pega’s predictable AI, which is governed by enterprise workflow, inherently mitigates many of the risks associated with fully autonomous agents. Because our agents follow structured processes, their behavior is far more deterministic than that of unconstrained systems.
However, to build complete trust, we embrace a hybrid evaluation approach that combines the best of both worlds: the nuanced understanding of LLM judges and the precision of rules-based validation. This is perfectly suited for frameworks like the open-source tool DeepEval.
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LLM Judge (Probabilistic Evaluation): For qualities that require human-like judgment, we leverage an LLM to act as the “judge.” This is ideal for evaluating:
- Hallucinations: Is the agent inventing facts not grounded in the provided context?
- Answer Relevancy & Contextual Appropriateness: Is the response on-topic and logical within the flow of the conversation?
- Toxicity & Bias: Does the agent’s output contain harmful or unethical content?
- Role Adherence: Is the agent staying on-brand and maintaining its designated persona?
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Rules-Based (Deterministic Evaluation): Because Pega agents are guided by workflow, many critical functions can and should be validated with precise, binary logic. This approach is used for:
- Tool Correctness: Did the agent call the correct tool or workflow at the right step in the process?
- Task Completion: Did the agent successfully reach the intended end state of the workflow?
- Latency & Performance: Did the agent’s response time exceed a predefined threshold?
This dual strategy allows us to test both the subjective quality of the agent’s conversational abilities and the objective correctness of its actions within the Pega ecosystem.
In Practice: Testing a Pega Agent with DeepEval
Let’s walk through how the DeepEval framework is used to test a Pega self-service agent designed to help a financial services customer dispute a credit card transaction. This example uses a custom application built in Python to demonstrate how to evaluate the Pega agent.
Capturing the “golden truth”
Instead of hand‑coding brittle test scripts, the process begins by capturing a golden conversation, which represents the “perfect conversation” for an agent:
- A developer performs an ideal interaction with a Pega agent
- Pega logs the full session, identified by a conversation ID
- A capture utility calls the Pega DX API to extract the full interaction history
The result is a structured golden session that represents how the agent should behave. This includes not only the conversational turns, but also:
- Which workflows, tools, or step agents were invoked
- Latency per turn
- Key conversational gates and transitions
Replaying and evaluating behavior
That golden conversation is then replayed against the current version of the agent:
- Deterministic checks confirm the correct tools and workflows are invoked
- DeepEval applies LLM‑based metrics such as relevance, faithfulness, and hallucination detection
Rather than asking “did the response match exactly?”, the system asks the more meaningful question: does the behavior still meet our intent and safety criteria?
Detecting drift and operationalizing safety
Each evaluation run produces a detailed scorecard:
- Pass/fail outcomes across qualitative and deterministic metrics
- Explicit detection of tool or workflow drift
- Latency regression analysis
- Explainable hallucination findings
These results can feed directly into CI/CD pipelines as a go/no‑go signal—and accumulate over time to support trend analysis and proactive governance.
In an AI‑driven enterprise, testing, monitoring, and safety are no longer optional safeguards—they are core capabilities.
By combining workflow‑guided agents with modern evaluation frameworks like DeepEval, Pega Predictable AI replaces subjective trust with objective proof. The result is not just more powerful AI, but AI that is governable, auditable, and safe to operate at scale.
That is what it means to be enterprise‑ready in an AI world.
What Are Your Thoughts?
As organizations navigate this evolving landscape, it’s important to consider how you’re approaching agent and LLM application testing, as well as the strategies you’re implementing for observability. If you or your organization is already on this journey, I encourage you to share your experiences, questions, and insights in the comments below.