Introduction
Testing software used to be a straightforward process. Engineers worked with predictable inputs and expected defined outputs. If you press a button, a specific action occurs. This method checks if the code runs as written. However, agentic AI systems represent a fundamental change from this model. These systems do more than just execute commands; they plan, reason, and use tools to make their own decisions. Their behavior is emergent and not always predictable, creating new challenges for quality assurance. We are no longer just testing code, but evaluating the quality of an agent's independent choices.
Given this shift, testing agentic AI cannot be confined to a single department. Relying on separate engineering or product teams to test these systems in isolation is not only inefficient but also risky. The agent might function technically but fail to meet user needs or business goals. Therefore, a new approach is necessary. The only way to ensure AI agents are effective, reliable, and properly aligned with their intended purpose is through tightly integrated collaboration between product and engineering teams. This partnership ensures that the agent's autonomous actions are continuously measured against both technical standards and real-world user value, making evaluation a shared responsibility.
What is Agentic AI and Why Evaluation Matters?
Agentic AI is AI systems that act on their own to accomplish goals with minimal human guidance. These systems can observe their environment, make decisions, and learn from the results of their actions. Since these agents operate independently, evaluating them is necessary to ensure they are reliable, safe, and effective.
Here’s why evaluation is so important:
It verifies that an agent’s decisions are accurate and aligned with its intended goals.
It builds user trust by confirming the agent behaves reliably and predictably.
It helps find and fix potential biases, security vulnerabilities, or other risks before they become serious problems.
It ensures the agent remains stable and performs correctly even in unexpected situations.
Understanding Prospectives of Product and Engineering
3.1 The Product Team's: Is It Solving the Right Problem?
The product team's primary focus is on user success and business value. They are concerned with whether the agent solves a genuine user problem and contributes to the overall goals of the business. Their evaluation centers on the external quality of the agent's output and its impact on the user experience.
Their core evaluation questions measure the agent's effectiveness from a user's point of view. Does the agent correctly interpret what the user wants to do ? Does it successfully complete the user's intended task, and is the interaction natural and trustworthy ? These questions help determine if the agent is a useful and reliable tool for the end user.
Key Metrics:
Intent Resolution: Did the agent understand and achieve the user's underlying goal?
Task Completion Rate: This is the percentage of user journeys that the agent successfully concluded.
Unclear Verifications: This assesses contextual correctness, where an output may not be exact but is still helpful and accurate in context.
User Feedback & Satisfaction Scores: These are qualitative and quantitative measures of the user experience.
3.2 The Engineering Team's: Is It Solving the Problem Right?
The engineering team concentrates on the technical integrity, performance, and stability of the AI agent. Their main goal is to ensure the system is built correctly and functions efficiently and reliably from a technical standpoint. They look at the internal processes of the agent to validate its construction and behavior.
Core evaluation questions for engineers focus on the technical execution of the agent's tasks. Is the agent's reasoning sound and logical ? Are its actions, like making API calls, technically correct and secure ? The team also questions if the system is efficient in its use of resources and stable under various conditions.
Key Metrics:
Task Adherence & Planning Accuracy: Did the agent follow its generated plan and break down the task correctly?
Tool Call Accuracy: This measures the precision, recall, and correctness of API calls and function usage.
Hallucination & Error Rate: This is the frequency of the agent generating factually incorrect information or failing a step in its process.
Efficiency & Cost: This tracks latency, token consumption, and the monetary cost per task.
A Practical Guide to Product and Engineering Collaboration
For agentic AI, the old "over-the-wall" handoff between product and engineering teams no longer works. These systems are too dynamic. Success depends on teams working together from day one to build and test agents that are both technically sound and meet user needs.
Here is a practical guide to making that collaboration happen.
4.1 Start with Shared Goals, Not Separate Roadmaps
Collaboration begins by defining what a "good" agent does. The product team brings a deep understanding of the user's problem and the business goals. The engineering team knows the technical possibilities and limitations of the AI.
Instead of writing separate documents, both teams should work together to answer key questions:
What specific, multi-step task should the agent complete for the user?
How do we measure success? Is it task completion rate, decision accuracy, or something else?
What are the technical guardrails? For example, which tools can the agent use, and what data can it access?
This creates a shared vision that guides both development and testing.
4.2 Design Evaluation Scenarios Together
Testing an agent's real-world performance requires more than just technical unit tests. It requires realistic scenarios that reflect what users will actually do.
Product managers should write user stories that outline ideal paths. For example, "A customer service agent should be able to access an order number, check its shipping status, and draft a notification email to the customer."
Engineers can then build on these stories by adding technical edge cases. What happens if the shipping API is down? What if the order number is incorrect?
When both teams contribute to the test cases, you get a much clearer picture of how the agent will perform under both perfect and imperfect conditions.
4.3 Create Fast Feedback Loops
Agentic systems learn and evolve, which means you cannot wait until the end of a sprint to test them. Teams need a continuous conversation.
This can be done through:
Combine reviews of agent logs: The product manager can review an agent's decision-making steps to see if they align with business logic, while the engineer checks the technical execution.
Interactive demos: Engineers can run the agent live, allowing the product team to provide immediate feedback on its behavior.
This approach helps teams spot and fix issues with the agent's reasoning much faster than traditional testing methods.
4.4 Review Agent Decisions as a Team
When an agent does something unexpected, it presents a learning opportunity for the entire team. It's not just a "bug" for engineering to fix.
The review process should be a combine effort:
Engineering investigates the technical side: What data did the agent use? Which function did it call? Why did it choose that path?
Product provides the business context: Was the agent's decision actually wrong, or just surprising? Did it follow a business rule or just find a creative solution?
By analyzing these events together, teams can refine the agent's core logic and improve the evaluation metrics to catch similar issues in the future.
How Product and Engineering Evaluate Agents Together
Layer 1: Foundational Observability
Comprehensive Logging & Tracing: To understand an agent's behavior, you must implement structured logging at every step of its reasoning loop. Using a platform like Future AGI, you can record the agent's thought process, its plan, the input for any tool it uses, and the resulting output. This creates a complete and understandable record of each action.
Engineering's Role: The engineering team is responsible for building the instrumentation to capture this data. They create the tools that produce detailed traces of the agent's decisions, its interactions with APIs, and any changes in its internal state.
Product's Role: The product team defines which user-journey milestones are critical to track. They specify the metadata that must be logged, which allows the entire team to analyze task success from a business and user perspective.
Layer 2: Multi-Dimensional Benchmarking
Synthetic & Adversarial Benchmarks (Engineering):
Engineers can use tools like Future AGI to generate synthetic data that simulates rare edge cases, contradictory user goals, or faulty API responses.
They also perform adversarial stress tests to find vulnerabilities like prompt injection, data leakage, and improper error handling.
Real-World & Human-in-the-Loop (HITL) Benchmarks (Product):
Product teams create "golden datasets" from real user interactions to establish a baseline for regression testing, ensuring updates do not break existing functionality.
They use HITL evaluation for specific scenarios where automation cannot accurately judge factors like tone, helpfulness, or brand alignment.
Layer 3: The Automated Evaluation & Feedback Pipeline
Co-owned Tech Stack: Product and engineering teams should jointly select and manage an evaluation and observability platform like Future AGI. This ensures both teams work from a single source of truth.
Automated Evaluators:
Engineering Sets Up: Engineers create code-based evaluators to check for technical correctness, such as proper JSON formatting, adherence to API schemas, and factual consistency against a database.
Product Sets Up: Product teams use LLM-as-evaluator models to assess output for summarization quality, relevance, and alignment with user intent.
Continuous Improvement Loop: A feedback mechanism should be established where evaluation results are automatically routed into a fine-tuning pipeline. This process allows the agent to continuously learn from its mistakes and improve its performance.
Figure 1: Product and Engineering Evaluation Cycle
Conclusion
Agent ecosystems will become more connected and business-ready, moving toward "agentic meshes" that let agents find, interact with, and work together safely on a large scale. Evaluation methods will go from single-score benchmarks to multi-dimensional frameworks that look at reasoning chains, task recovery, and compliance in the real world. Hybrid human-AI evaluation will use both automated pipelines and checks by experts to keep an eye on ethics, find small biases, and make sure the deployment is safe. Industry groups will create open standards like SWE-bench Verified that will share clear benchmarks.
Next step - Set up a call with Future AGI to test and improve your agent workflows today.
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