What’s the single biggest architectural risk when scaling from one agent to many?
For Anjali Chhabra re: agentic AI for decision making, can you please elaborate on this?
How do you evaluate tools for agentic AI of test management?
Is there any data showing that production defects are reduced by usage of AI/Agents in QA Lifecycle?
How do you enforce compliance guarantees in inherently unpredictable AI systems?
When should humans override agents, and how do you architect that fail-safe?
How do you trace accountability when multiple agents jointly make a decision?
What governance models keep enterprises in control while using autonomous agents?
What architectural pattern do you consider essential for resilient agentic AI?
Will AI outsmart human intelligence? If yes, in which areas? And if no, in which areas?
How do you design a shared “memory layer” so multiple agents don’t work at cross-purposes?
What governance mechanism ensures agents respect enterprise boundaries without stifling innovation?
If Agentic AI is making autonomous testing and quality decisions, what practical guardrails should enterprises set so they don’t lose control over critical functions?
Any best practices to build trust/accountability with partners on agentic AI solutions?
How can enterprises ensure that agentic AI systems comply with data privacy regulations?
That’s a great question, and one that’s becoming really important as teams start experimenting with agentic AI in testing and development.
The best way to make sure your test data and environments actually reflect real-world conditions is to build production-like sandboxes. Think of them as safe replicas of your live system, they should mirror your actual data structures, configurations, and workflows as closely as possible.
Use synthetic data that statistically matches what you see in production, but without exposing any sensitive information. Also, don’t forget to bring in telemetry traces and real failure patterns from production, these help your AI agents encounter the same edge cases your users might face.
It’s equally important to test under different conditions, like network delays, authentication changes, or third-party service failures. Setting up these environment permutations in automated test matrices ensures your AI performs reliably under all sorts of real-world scenarios.
Finally, make it a habit to continuously refresh your datasets with anonymized production samples and keep a catalog of rare or tricky edge cases you’ve seen in the wild. Over time, this library becomes your secret weapon for building and validating smarter, more resilient AI systems.
I like this question, moving from experiments to enterprise-grade agentic AI isn’t about taking one big leap, but rather a series of smart, controlled steps.
Start by running small, well-defined pilot projects where the goals and success metrics (KPIs) are crystal clear. These pilots help you learn fast and build confidence without risking large-scale operations.
Next, make sure you have strong monitoring systems in place. If something goes off-track, your team should be able to roll back quickly using predefined playbooks this keeps things safe and manageable.
Once your pilots are stable, focus on governance and testing. This means having a proper process for model versions, approvals, and SLAs, so everyone knows what’s running in production and what standards it meets.
For critical use cases, keep humans in the loop their oversight ensures decisions remain safe and trustworthy. As your systems consistently perform well on key metrics like reliability, safety, and transparency, you can then gradually increase automation and give the AI more autonomy.
In short, think of it as building a reliable system layer by layer starting with controlled pilots, adding safety nets, and earning your way to full-scale, enterprise-grade AI.
Hey Everyone👋
The biggest risk lies in getting too comfortable after an early success. Many teams assume that if an AI pilot project worked well, it’ll automatically scale across the enterprise, but that’s rarely the case. When you move from a small proof of concept to full-scale deployment, hidden cracks start to appear, things like weak integrations, inconsistent data quality, and missing governance checks. The real challenge isn’t just about the AI model itself; it’s about making sure the systems, processes, and compliance structures around it are strong enough to support it at scale.
A practical way to handle this is by setting tolerance ranges instead of expecting a single “right” answer every time. Think of it like defining acceptable boundaries for how the AI can behave. Then, run scenario-based stress tests that simulate different real-world situations to see how the system reacts under pressure.
It’s also important to record decision traces basically keeping a log of how and why the system made each decision. Later, you can replay those decisions to compare how its behavior evolves. Finally, have a review loop in place where both the AI’s outputs and human judgments are analyzed together, ensuring that any drift in behavior over time is caught early and corrected.
This mix of tolerance checks, testing, and continuous review helps maintain trust and reliability, even when the system is learning and changing.
A good way for organizations to balance Agentic AI’s autonomy with human oversight is to match the level of autonomy to the level of risk.
For example, you can let AI handle low-risk, repetitive tasks on its own, while keeping human review or approval for high-impact decisions. It’s also smart to have checkpoints or “safety nets” things like an undo option, a fail-safe mode, or clear audit logs showing what actions were taken and why.
This approach ensures that teams can enjoy the speed and efficiency of autonomous systems while still maintaining trust, transparency, and control over critical software functions.