Infosys TechCompass

Agentic AI

Trend 11: Evaluation becomes a continuous loop

Teams are shifting from ad hoc to continuous evaluation loops that use golden sets, synthetic edge cases, rubric-based scoring, and human review to gate changes and monitor live performance.

A large bank has replaced its quarterly testing with nightly evaluation suites. These tests check whether AI tools respond within time limits, comply with policies, and retrieve information accurately. Only the updates that meet all thresholds are moved forward, helping the bank maintain consistent performance and customer trust while avoiding costly errors.

Agentic AI

Trend 12: Simulation emerges as the path to autonomy

Leading teams are validating complex multiagent behaviors through digital twins before releasing them into production.

An energy utility uses a digital twin of its power grid to train agent swarms for fault management. Here, agents learn to reroute electricity, balance loads, and optimize switching plans under a variety of failure scenarios — from equipment malfunctions to sudden demand spikes. Once proven safe and effective, these policies are carefully promoted to live operations with built-in safeguards, improving grid resilience, minimizing downtime, and handling unexpected events without risking service disruptions.

Agentic AI

Trend 13: AI-driven agents push toward greater autonomy

AI is transforming simulations from static, rule-based models into adaptive ecosystems where agents learn, coordinate, and optimize in real time. Self-learning robots and digital agents trained in simulated environments now adjust to fluctuating conditions with minimal human intervention — improving flexibility, resilience, and efficiency.

A global logistics provider partnered with Infosys to deploy a multiagent simulation trained via reinforcement learning that dynamically rerouted shipments and tuned inventory under real-time disruptions such as bad weather and labor shortages. The program cut delivery delays by 20% and lifted peak-season satisfaction.

Agentic AI

Trend 14: Realism in AI-driven simulations increases

AI is pushing simulations toward lifelike fidelity, enabling virtual worlds that closely mirror physical complexity across automotive, aerospace, gaming, real estate, and retail. Deep learning boosts 3D visuals by improving textures, lighting, and physics; reinforcement learning creates more adaptive, human-like agents; synthetic data platforms like NVIDIA Omniverse generate diverse training data safely; and physics-informed AI speeds up complex simulations such as fluid dynamics with higher accuracy. Enterprises are integrating digital twins and real-time IoT feeds to continuously align simulations with live conditions, improving design quality, safety, and time-to-market.

Cross-functional collaboration between data science, engineering, and domain experts is becoming essential to tailor virtual environments for industry-specific needs and to operationalize insights across the life cycle — from concept and testing to operations and optimization.