Enterprise AI hit another turning point in 2025. What started as scattered pilots evolved into a coordinated push to deploy agentic AI systems across industries and business functions. These weren’t just assistants; they were decision-makers and executors. Automation adoption was always constrained in business processes that had some level of uncertainty. These same processes were ripe for agentic disruption. Agents came as adaptive problem-solvers and workflow orchestrators, introducing a fundamentally new way for businesses to operate.
How We Got Here
The foundation was laid in the years before. In 2023, open-source frameworks like AutoGen and LangGraph showed how autonomous, LLM-powered agents could work together to tackle multi-step problems. Enterprises took notice. By early 2024, major cloud providers began embedding memory-augmented, tool-using agents into their platforms, making secure enterprise integration easier than ever.The Scale Shift
What changed in 2025 was both scale and intent. Gartner’s October 2024 announcement naming agentic AI a top strategic trend for 2025 set the tone, defining it as systems that “learn, adapt, and act autonomously to perform business tasks.” New enablers of agentic AI arose, like small language models, better infrastructure, more robust agentic guardrails and more mature frameworks, agentic protocols like the MCP and A2A. This leap was also powered by advances in foundation models. Releases like GPT-5 and Claude 3.5 brought longer memory windows, stable tool integration, and reliable planning capabilities. Combined with agent platforms such as ReAct and ADEPT, these models could interpret goals, plan sequences, and dynamically invoke APIs or data sources. Enterprises could now delegate entire workflows, not just isolated tasks.Also Read: TCS steps on the gas for meaningful M&A as part of AI facelift
From Speed to Proactivity
Agentic AI didn’t just make processes faster; it made them proactive.- In finance, agents reconciled anomalies across distributed ledgers.
- In supply chains, they balanced inventory using real-time demand signals.
- In customer operations, they navigated systems, validated policies, and escalated only edge cases.
Early results were striking: McKinsey cited a 25 percent drop in average call times for customer service and up to 60 percent faster content review cycles. Some projects even cut effort by half for specific tasks. Other McKinsey reports revealed that 39 percent of large enterprises were already scaling agentic AI across multiple business units, with another 25 percent deploying it in at least one function. Agentic AI had officially moved from experimentation to execution.2025 wasn’t about flashy demos; it was about measurable impact. Banks deployed agents for fraud detection and audit routing. Logistics firms used multi-agent systems to optimise container loads and plan routes based on fuel costs and customs delays. These weren’t proofs of concept; they were operational systems tied to KPIs.
Governance Gets Serious: Responsible Agency by Design
With autonomy came new risks: model drift, unauthorised actions, and accountability gaps. Enterprises responded with governance frameworks like agent registries, task boundaries, and real-time decision logs for traceability. The EU’s AI Act reinforced human-centric guardrails, especially in sensitive domains like healthcare and employment.Rather than replacing roles, agentic systems augmented them. Employees shifted from execution to orchestration, validating outputs, setting boundaries, and innovating. Organisations trained staff to work alongside agents as supervisors and fail-safes.
Ethics moved to the forefront. Enterprises embedded transparency, auditability, and escalation logic into agent workflows. Principles like intent clarity and red-teaming became standard practice.
A New Era
In 2023 and 2024, AI was often framed as a cost-cutting tool. By 2025, it was seen as a strategic asset, enabling resilience, accelerating transformation, and navigating uncertainty. This wasn’t about one breakthrough. It was about readiness. Infrastructure matured. Models became capable. Organisations got serious. And as agentic AI moved from labs to live production, it redefined what enterprises could do, and how they defined doing it.The author is the EVP - Global Services Head, AI and Industry Verticals at Infosys.
