India is set to spearhead this transformation. A recent Ernst and Young report titled “Is India ready for Agentic AI? The AIdea of India: Outlook 2026” reveals that nearly half of Indian enterprises (47%) currently have multiple live Generative AI (GenAI) applications, with 23% still in the pilot stage. With 76% of business leaders anticipating a significant impact from GenAI, it’s clear there’s a strong appetite for innovation.
However, transitioning from a chatbot to a fully autonomous business agent hinges on one essential element: context.
Context engineering is the new gold standard
To thrive, agentic AI must do more than engage in conversation; it must reason and yield business results. This necessitates connecting LLMs with contextually precise, real-time data. Without this connection, an agent remains a powerful engine lacking direction.
This leads to context engineering, the practice of providing an AI agent with the most pertinent, high-quality information precisely when needed. Overcoming this challenge is critical, as data is frequently dispersed across numerous sources, both structured and unstructured, including databases, documents, emails, CRM systems, Slack messages, social media posts, log files, and transaction data.
To address this disparity, many AI innovators and developers are leaning towards unified platforms like Elasticsearch. This open-source solution serves as a vector database, search engine, NoSQL document store, and a monitoring and analytics tool, all in one. By storing both structured and unstructured data centrally and contextually, it simplifies the process of establishing logical connections rapidly. Consequently, developers can create robust and reliable agents based on their business data within minutes.
Building agents with less complexity
Creating an AI agent entails a complex system of diverse components, each serving distinct functions ranging from reasoning and context gathering to learning and collaborating with systems to perform tasks. By 2026, the development approach will gravitate towards a modular, “Lego-block” style.
Leading tech companies are now providing pre-assembled components through Agent Builders, enabling business users and developers to construct solutions without lengthy coding processes.
For instance, if a financial firm requires a tool to help private bankers identify clients at risk during a market crash, a developer can utilize an Agent Builder. This allows them to piece together the solution: a retrieval component to search client portfolios, a risk scoring calculation engine, a memory component that tracks clients who missed prior warnings, and a reasoning engine to analyze the correlation between liquidity and historical data. Finally, a guardrails component ensures the AI refrains from suggesting irreversible actions without human consent.
This modularity empowers organizations to implement complex AI solutions—from context to outcome—with unmatched speed.
Speed in observability and security
As AI agents acquire context, they become a pivotal force for organizational resilience. In the realm of IT and cybersecurity, responses to threats or outages are now measured in seconds, not days.
When an application outage or cyberattack occurs, it is frequently too late; consumer trust can be the cost of downtime. AI agents shift the focus from a reactive to a proactive approach.
In contrast to traditional rule-based systems, adaptive AI-driven software learns from context, including the behavior of new threats or application performance metrics from the last Black Friday sale. Equipped with this information, security and IT teams can deploy AI agents to proactively detect anomalies and resolve issues before they affect customers.
No agents without context
AI is evolving from a tool that assists professionals into a strategic force that enhances operations, decision-making, and innovation. However, with this autonomy comes the risk of inaccuracies. The success of an AI agent hinges entirely on the precision of its context.
Agentic AI and context engineering will be foundational in the next era of AI. Organizations that excel in supplying agents with unified, actionable data will cultivate a competitive edge that is hard to surpass—transforming the potential of AI into tangible business impact.
