How Agentic AI Changes Software Development and QA

Moving from code execution to goal pursuit

Traditional software follows a deterministic path: given input A, it produces output B. Developers write precise logic, and QA writes tests to ensure the logic or app behaves as expected. Agentic AI upends this model.

Instead of following hard-coded rules, agentic AI systems interpret high-level goals (e.g., “summarize this meeting” or “book the cheapest flight that arrives before noon”) and decide how to accomplish them. This decision-making involves:

• Planning: Breaking goals into steps.
• Tool use: Calling APIs, functions, or external services.
• Memory: Recalling past interactions or outcomes.
• Feedback loops: Evaluating and refining behavior based on results.

These capabilities make agents more flexible — but also harder to reason about. They don’t just execute; they act.

In traditional software, code execution follows a predefined, deterministic path. The system executes logic exactly as written — it doesn’t deviate, improvise, or make decisions beyond simple conditionals. Agentic AI, on the other hand, operates at a higher level of abstraction. When we say agents “act,” we mean that they:

• Interpret goals or instructions
• Decide how to accomplish them
• Plan multi-step strategies
• Choose which tools to use — and when
• Learn or adjust based on feedback or memory

This autonomy introduces flexibility — agents can solve problems in novel ways — but it also introduces non-determinism. The actions agents take depend on current context, prior memory, available tools, and how well they understand the task. As a result, the same input may lead to different behavior depending on subtle environmental changes. Developers and QA professionals must now test and debug intent, strategy, and behavior, not just code correctness.

Here’s a breakdown of how developers and QA teams need to shift their thinking to align with the ways agentic AI is changing how we work.

What Changes for QA?

1. Testing is no longer deterministic
Traditional QA assumes a known set of inputs and expected outputs. With agentic AI, behavior is less predictable. QA teams must test whether:

• The agent achieves the goal under different conditions
• It avoids undesired behaviors (e.g., unsafe tool use, incomplete responses)
• It recovers appropriately from failure states

Instead of static test cases, think in terms of scenario-based simulations and stochastic testing. From a QA perspective, we still need human creativity. At Applause, we rely on our community of testers to introduce unexpected variables and uncover edge cases. This type of testing is especially important for assessing systems built with agentic AI.

2. You’ll measure behavior, not just functionality
Agentic AI calls for new evaluation metrics. In addition to traditional QA KPIs, teams must assess metrics like accuracy, relevance, and hallucination rates. Some additional metrics to consider:

• Task success rate: Did the agent complete the goal?
• Tool efficiency: Did it take the most direct path?
• Factuality: Were its outputs grounded and accurate?
• Coherence and consistency: Was its reasoning logical and memory accurate?

Some metrics are automatable; others require human-in-the-loop validation. It can be difficult for something with autonomy to evaluate whether it did a good job; that’s subjective. And that’s where humans are good: at evaluating subjective versus objective results.

3. QA needs new infrastructure
Testing agentic systems demands purpose-built infrastructure that accounts for autonomous, non-deterministic behaviors and long-running tasks. Key components include:

• Sandboxes to run agents in controlled simulations: These environments allow QA teams to isolate agents and observe their behavior under predefined constraints. Simulated environments help ensure agents can be tested safely, especially when they control sensitive operations like financial transactions, external API calls, or user communication.
• Replay systems for analyzing past decisions: Similar to test replays in video games or log inspection tools, these systems record every decision the agent made, along with inputs, outputs, and tool usage. QA teams can step through an agent’s execution path to pinpoint where a decision went wrong or deviated from expected behavior.
• Synthetic goal generators to test diverse behaviors: Instead of manually crafting each test case, synthetic goal generators dynamically create variations of prompts, goals, and edge cases. This ensures broad coverage across different user intents and encourages exploratory testing that surfaces emergent issues.
• Observability tools to detect drift and anomalies: Just as APM tools monitor latency and resource usage, observability for agentic AI focuses on monitoring reasoning patterns, goal completion rates, hallucination frequency, and memory consistency over time. These tools uncover anomalies that indicate when agent behavior is degrading or deviating from acceptable bounds.

In this new landscape, QA becomes more like evaluating the performance of a junior analyst: understanding the rationale behind decisions, spotting poor reasoning, and coaching the system to improve. It’s no longer just about catching errors — it’s about fostering responsible and intelligent behavior.

Shared responsibility: Closing the feedback loop

Process-oriented tasks, like development and testing, lend themselves to agentic AI. One of the most exciting (and challenging) changes is that agentic systems can learn from their behavior. Logs, test failures, and user corrections can feed back into the system to improve performance.

This requires tight coordination between dev, QA, and AI/ML teams:

• QA tags examples of poor behavior → dev updates reward models
• Dev observes tool misuse → adds stronger validation
• Logs surface blind spots → new goals added to test coverage

We’re moving from a release-and-forget model to a continuous tuning loop, more akin to MLOps than traditional software delivery.

In order to get the most out of agentic AI, it’s important to understand its logic and limitations – which are still rapidly evolving.

Final thoughts: the new stack for autonomy

Agentic AI doesn’t just add intelligence to software — it demands new mental models, new design patterns, and new quality criteria. For developers, it means building safe, expressive environments for agents to operate. For QA, it means testing dynamic, goal-driven behaviors rather than static flows.

As these systems move from labs to production, embracing agentic principles will be a key differentiator. Teams that build the right tooling, observability, and collaboration around agent behavior will be best positioned to deliver intelligent, adaptive software that scales.