That’s an excellent question, and one that was thoughtfully discussed during the session.
As autonomous AI agents become more integrated into software development, the role of QA and governance teams will expand in meaningful ways. QA professionals will evolve into a blend of engineer, auditor, and data curator. They will design and manage test oracles to define expected outcomes, build adversarial test suites that challenge system behavior, and establish governance checks to maintain compliance and accountability.
For governance teams, having clear visibility is key. Dashboards that highlight model drift, confidence scores, and decision lineage will help them track performance and intervene whenever needed.
In essence, QA and governance teams will not just oversee these intelligent systems but work in close partnership with them ensuring that automation remains transparent, reliable, and aligned with enterprise objectives.
To move from just experimenting with LLM agents to actually guaranteeing their reliability, you need a structured approach.
Start by clearly defining your acceptance criteria what does “good enough” look like in terms of safety, speed (latency), and resource usage? Once that’s set, run shadow tests alongside real production traffic for an extended period. This helps you see how the system behaves in real-world scenarios without risking actual operations.
Finally, make sure every evaluation is reproducible that means keeping detailed records of the datasets, configurations, and results used in testing. This way, whenever you or your team revisit the setup, you can trace exactly how and why the agent performed a certain way before you push it live.
It’s all about building confidence step by step from controlled experiments to production-grade reliability.
Great question! Evaluating agentic AI in production isn’t just about accuracy it’s about real-world performance and impact. Look at robustness (how it handles unexpected changes), recoverability (how fast it recovers from errors), explainability (how clear its decisions are), latency under load, and cost per decision. Also, track how often humans need to step in and connect these insights to business results like reduced errors and faster resolutions.
A good way for organizations to strike the right balance between AI autonomy and human oversight is to set up clear guardrails and approval workflows. For instance, let AI agents handle routine decisions, but whenever something goes beyond defined limits or impacts critical quality areas, it should automatically trigger a human review.
Teams can also build role-based approvals where specific team members validate important actions or outcomes before they move forward. And just as important, every human intervention or override should be logged and tracked, so there’s always a transparent trail of who made what decision and why.
In the end, accountability in quality assurance isn’t just about writing the right code it’s about maintaining a clear, auditable process that keeps humans in control while still letting automation do its job efficiently.
Hey all
Great question! Creating realistic test data and environments for agentic AI comes with several challenges. Real-world data changes constantly, privacy rules limit access to real user data, and mock environments often miss real-world complexity. The way forward is to use synthetic data to bridge gaps, apply privacy-safe sampling, and build flexible environments that can simulate real-world failures, ensuring the system performs reliably, even when things get unpredictable.
Hello,
As AI systems take on more routine testing tasks, the role of human QE professionals will evolve into more strategic functions, focusing on test design, model validation, and risk analysis. To stay relevant, professionals should strengthen skills in data literacy, MLOps fundamentals, prompt engineering, interpretability tools, and domain-driven testing. Developing a deep understanding of the product and business context will remain essential to ensure quality aligns with real-world needs.
Greetings,
The main concern with deploying autonomous AI at scale is the silent drift when systems gradually deviate from expected behavior without immediate detection. These unnoticed shifts can accumulate over time, leading to cascading failures that only become visible once a major issue occurs.
One of the biggest challenges in bringing autonomous AI agents into older, complex systems is that these legacy setups weren’t really built to “talk” to modern tools. Many of them don’t have proper visibility (observability) or reliable APIs you can safely call again and again without breaking something.
To make this work, teams often have to add new layers like telemetry for better tracking, consistent data contracts so everything speaks the same language, and safe adapters or façades that act as bridges between the old and the new. It’s a bit like connecting a modern smart device to an old electrical system you need the right converters to make sure everything works smoothly without blowing a fuse.
Hello everyone,
To effectively manage data quality and governance for agentic AI, organizations should establish a centralized data catalog to maintain visibility across all sources and ensure proper data lineage tracking. Implementing automated validation checks and role-based access controls helps maintain accuracy and security. Additionally, continuous monitoring for data drifts, along with timely alerts and retraining, ensures the system remains consistent and reliable.
That’s a great question and one that almost every engineering team is asking today.
Both “build” and “buy” have their place. If your system needs deep domain-specific logic or tight integration with internal tools, building your own makes sense. You get full control and flexibility to shape it exactly how your workflows demand.
But if the goal is to move fast and tap into proven observability and RCA capabilities, buying is often the smarter move. Tools like Datadog already handle a lot of complexity and give you a mature foundation right away.
What many teams are doing now is a mix of both buying the core observability platform to get up and running quickly, and then building custom automation or remediation layers around it. This hybrid model brings the best of both worlds: speed from what’s already built, and adaptability from what you create in-house.
Hello,
Yes, Agentic AI can detect anomalies and trigger recovery playbooks when unexpected failures occur. However, for it to respond effectively, it requires proper adversarial training and well-defined fallback policies. Without these guardrails, the system may not handle edge cases safely or reliably.
Hello everyone,
When designing agentic AI architectures that operate autonomously while ensuring security and compliance, a few key principles are essential. Start with the principle of least privilege, ensuring access is strictly limited. Secure all data through encryption in storage and transit, and maintain audit logs for transparency. Use tokenized credentials instead of direct passwords, and include explainability layers for decision traceability. Finally, enforce policies like data masking and consent checks within the decision flow to maintain privacy and compliance throughout operations.
I’d say most teams are in the “walk” stage right now meaning they’ve moved beyond small experiments and are running pilot projects in real or production-like environments. They’re testing out how these AI-powered tools fit into their workflows, keeping a close eye on what’s working and what needs fine-tuning.
If your team is still trying things out with demo data or running isolated experiments, that’s totally fine, it just means you’re in the “crawl” phase, getting comfortable and laying the groundwork before taking bigger steps.
Hello,
Organizations can maintain accountability by clearly assigning ownership for every responsibility such as who approves model versions, reviews incidents, and serves as the emergency contact. Along with this, maintaining immutable logs and role-based playbooks ensures transparency and quick resolution when issues arise.
Thank you for the question.
Partial self-healing in test systems is already becoming a reality, especially for tasks like fixing flaky selectors or regenerating mocks. However, fully autonomous test creation without human validation remains risky for critical workflows. We can expect a gradual increase in automation, but human oversight will continue to play an important role in ensuring reliability and accuracy.
Enterprises can start by deploying intelligent agents that continuously monitor how workflows and processes are performing. These agents can spot patterns, identify inefficiencies, and suggest ways to make things run smoother whether it’s automating repetitive steps or reordering tasks for better efficiency.
Once a potential improvement is found, the agent can run controlled A/B tests to see if the change actually makes a difference. The key is to keep humans in the loop every major or permanent change should go through human review and approval to ensure it aligns with business goals and compliance needs.
This way, organizations get the best of both worlds: automated insights that drive efficiency, and human oversight that ensures reliability and trust.
That’s a great question and a really important one when working with intelligent systems that make their own decisions.
The key is to make sure every local agent understands the bigger picture it’s part of. You can do this by setting clear global policies and constraints that define what “good” looks like for the organization as a whole.
Then, use a hierarchical setup local agents can suggest actions or optimizations, but a central orchestrator always steps in to validate those decisions against enterprise-level goals before anything goes live.
In short, let agents think locally, but always have a system in place that keeps them aligned with the company’s overall objectives.
That’s a great question, and it really comes down to understanding risk and trust in your system.
Start by automating the low-risk, day-to-day tasks things like operational optimizations or repetitive checks where the impact of an error is minimal. These areas are perfect for full automation because they save time without much downside.
But when it comes to user-facing changes or anything that could affect compliance, data integrity, or business-critical outcomes, it’s important to keep a human in the loop. Having a review or approval step here adds a layer of assurance that’s worth the extra effort.
Over time, as your automation matures and you gain confidence in its reliability, you can adjust the balance, gradually expanding automation into more complex areas while still keeping clear checkpoints for sensitive decisions.
It’s all about building trust step by step, automate where it’s safe, involve people where it matters most.
Hello everyone,
When securing AI agents in enterprise automation pipelines, focus on key principles like credential vaulting to protect sensitive data, establishing clear service identities, enforcing rate limiting, and applying least privilege access. Additionally, ensure proper input sanitization to prevent prompt injections and maintain detailed audit trails for accountability.
These measures help maintain security, transparency, and control across the automation process.
It’s all about finding the right balance between trust and oversight. Let AI handle the routine, repetitive tasks where the risk is low and outcomes are predictable that’s where autonomy shines. But when it comes to exceptions, policy-related scenarios, or decisions that can have a big business impact, it’s important to keep humans in the loop. This way, you get the best of both worlds: efficiency from automation and assurance from human judgment.