The shift in agentic AI is not that the models are getting smarter, it is that they have almost stopped waiting for instructions. These AI systems are becoming independent to a point where they can plan tasks, call tools, iterate on outputs, recover from errors, and move towards a goal – without constant human prompting. If you are still asking what is agentic AI, this is what it does. And this shift is visible in how local and global companies are embedding it into their workforce.
To give you a context on the scale at which agentic AI is core to how businesses are functioning – in India, a Thoughtworks global survey of 3,500 C-suite and IT leaders found that 49.2% of Indian executives expect a 15% or higher revenue uplift from AI within the next five years.
From that same study, 48.6% of Indian leaders named agentic AI as a top strategic priority, far ahead of the global average of 35%. Similarly, EY’s AIdea of India: Outlook 2026 report surveyed 200 enterprise leaders and found that 24% have already deployed agentic AI, 47% have multiple GenAI use cases live in production, and 91% cited speed of deployment as the single biggest factor in their buy-vs-build decisions.
This is a push from pilot to production, and this is accelerating across sectors.
But here is where the picture fractures. India needs close to one million AI professionals by end of 2026. However, fewer than half of that number is actually qualified today. That is a 53% talent gap, according to TeamLease Digital’s Skills & Salary Primer (FY 2025-26).
For every 10 open GenAI roles in India, only one qualified engineer is available.
In this article, we will discuss why you must understand the technical side of what is agentic AI, the frameworks you can build with, real-world deployments across sectors, and where the technology is headed next. We will start with understanding what agentic AI means, and the AI landscape state in India.
What is Agentic AI: Definition and Indian Market Analysis
A Quess Corp analysis of 28,000+ job postings found that new roles like AI Orchestration Engineers, Agent Behaviour Analysts, and Multi-Agent Architects are now appearing in hiring data. Indian tech cities like Bengaluru and Hyderabad alone are accounting for 62% of these positions. On the other hand, mid-to-senior professionals are making up over 70% of agentic AI hiring, with salary premiums of 20–28% over comparable AI roles.
The disconnect is clear. Indian enterprises are deploying agentic systems while simultaneously hiring for agentic roles. This might have led to executive optimism which is currently among the highest in the world, but the talent pipeline is yet to catch pace.
Hiring is no longer centered on entry-level roles. Over 70% of demand is for mid-to-senior professionals, with Global Capability Centers driving more than half of this demand. This reflects a clear move toward complex, system-level AI implementation. The result is a paradoxical market: hyper-growth constrained not by adoption, but by the scarcity of people who can design, orchestrate, and scale agentic systems.
The way forward to is to understand the technical aspects of agentic AI. Once professionals and graduates have complete understanding of these systems, how they function, how to build and deploy, and most essential, how to leverage for business decisions, the demand vs. supply gap might close in.
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Agentic AI: Definition
The word agentic comes from “agency,” meaning the capacity to act independently and make choices. In AI, agentic meaning refers to a system that exhibits this capacity: it does not simply generate content on demand. It pursues objectives.
Here is the key distinction in one line: Generative AI generates content on demand. Agentic AI pursues objectives autonomously.
When you ask ChatGPT to summarise an article, that is generative AI at work. When a system monitors your inbox, identifies urgent supplier emails, cross-references them against your inventory database, drafts a response, and schedules a follow-up call all without you writing a single prompt, that is agentic AI at work.
PS: For a foundational understanding of how AI, ML, Deep Learning, and NLP interconnect, enrol for our Full Stack AI Course, which covers these building blocks in depth.
What Makes an AI System Agentic?
Not every AI system qualifies as “agentic.” A system earns that label when it demonstrates four core capabilities working together. Understanding what is agent in AI requires examining each pillar.
1. Autonomy and Goal Orientation
An agentic system receives a goal, not a step-by-step instruction set. It decides how to achieve that goal, selects its approach, and adjusts the plan if something goes wrong. A customer-service agent that independently resolves a refund request, checks order history, and updates the CRM illustrates this pillar in action.
2. Reasoning and Planning
Reasoning is what separates an agentic system from a script. The agent breaks a complex goal into smaller sub-tasks, sequences them logically, and re-plans when it encounters unexpected results. Modern agents often use the ReAct (Reasoning + Acting) pattern: the agent thinks through a step, executes an action, observes the result, and reasons again before the next action.
3. Tool Use and Environment Interaction
An agentic system does not operate in a vacuum. It calls APIs, runs database queries, browses the web, writes and executes code, and interacts with external services. This is what makes agents practical. A financial analysis agent, for example, might pull live market data from an API, run a valuation model in Python, and send the output to a Slack channel.
4. Memory (Short-term and Long-term)
Memory gives agents context. Short-term memory (conversation context or scratchpad) helps the agent track what it has done within a single task. Long-term memory (vector stores, databases) lets the agent recall past interactions, learned preferences, or prior decisions across sessions. Without memory, every task starts from scratch.
Technical Note: Formally, an agent’s behaviour can be modelled as a Markov Decision Process (MDP). At each state, the agent selects an action to maximize expected discounted reward, with a discount factor γ governing the trade-off between immediate and future returns. This formalization underpins the design of reward signals and planning horizons in production-grade agents.
The Agentic AI Loop: How Does Agentic AI Work?
At its core, every agentic system runs a loop: Perceive, Reason, Act, Observe, Repeat. The agent receives input (a goal or a new observation), reasons about what to do next, takes an action (calls a tool, generates text, queries a database), observes the result, and then loops back to the reasoning step.
This loop is fundamentally different from a single-pass language model that generates a response and stops. The loop continues until the agent decides the goal is complete or a human intervenes. Let us first understand what is the difference between single-agent and multi-agent architectures.
Single-Agent vs. Multi-Agent Architectures
A single-agent architecture assigns one LLM-powered agent to the entire task. This agent handles all reasoning, tool calling, and output generation. This works well for simple workflows. For instance, a code assistant can write as well as debug a function. This is a single-agent architecture.
Multi-agent architectures distribute work across multiple specialised agents. Each agent has a specific role: one researches, another writes, a third reviews. An orchestrator (sometimes called a “supervisor” or “puppeteer”) coordinates them. This mirrors how human teams operate.
Speaking practically, multi-agent setups are more capable of handling complex, multi-domain tasks reliably. Infact in 2025, multi-agent systems commanded a 53.3% share of agentic AI deployments.
Now consider a real-world scenario – An enterprise wants to automate quarterly earnings analysis. A single agent would struggle to pull financial data, compare it against competitor filings, write a summary, and generate charts, all within a single reasoning chain. But, a multi-agent setup easily assigns a data retrieval agent, an analysis agent, a writing agent, and a charting agent, each working on its strength to get the task done.
Also read: AI Agents: Types, Usecases, and How to Build (Step-by-Step Guide)
While AI agents are evolving to work independently, the line between generative AI, traditional AI, and Agentic AI is becoming more distinct. These may have connections at some level, but conceptually, these are now three stages of how AI has evolved.
Agentic AI vs. Generative AI vs. Traditional AI: Technical Comparison
Generative AI is reactive. You prompt it, it responds. But, agentic AI is proactive. You give it a goal, and it figures out how to get there. On the contrary to these, traditional AI is deterministic and narrow.
Each paradigm serves different use cases, and many production systems combine all three to get the best out of AI.
So, how does agentic AI vs generative AI compare? And where does traditional (rule-based) AI fit? The table below provides a technical comparison across eight dimensions.
| Dimension | Traditional AI | Generative AI | Agentic AI |
|---|---|---|---|
| Core Mechanism | Rule-based, deterministic | Pattern generation from training data | Goal-driven autonomous execution |
| Decision Making | Pre-programmed if/then logic | Single-pass inference | Multi-step reasoning with replanning |
| Tool Use | None or hardcoded integrations | Limited (function calling) | Dynamic tool selection and chaining |
| Memory | State machines, databases | Context window only | Short-term + long-term memory stores |
| Adaptability | Requires manual rule updates | Adapts via fine-tuning or prompting | Self-corrects during execution |
| Autonomy Level | Zero. Human triggers every action | Low. Responds when prompted | High. Pursues goals independently |
| Example | Spam filter, fraud rule engine | ChatGPT writing an email | Agent managing end-to-end hiring workflow |
| Architecture | Decision trees, expert systems | Transformer-based LLMs | LLM + planner + tools + memory + orchestrator |
Now that we are clear of where agentic AI fits, let us look into the frameworks and tools of agentic AI.
Agentic AI Frameworks and Tools
An agentic AI framework is at the core of any (agentic) system that you want to build. Over the years, the AI ecosystem has matured by leaps and bounds, offering multiple options to choose from (depending on the type of system you want to build). Below are the most common agentic AI frameworks:
1. Open-source Frameworks
- LangGraph (by LangChain) is the most production-mature framework as of March 2026, having reached v1.0 GA in October 2025. It models agent workflows as directed graphs with nodes (agent functions) and edges (transitions). Companies like Uber, LinkedIn, and Klarna run LangGraph agents in production. It excels at complex, stateful workflows with human-in-the-loop approvals and is the top choice for regulated industries that need audit trails.
- CrewAI takes a role-based approach: you define agents with roles, goals, and backstories, then organize them into “crews.” With over 44,600 GitHub stars and 12M+ monthly PyPI downloads, CrewAI is the fastest framework for prototyping. It shipped native MCP (Model Context Protocol) and A2A (Agent-to-Agent) support in v1.10. The trade-off: it lacks fine-grained state persistence for long-running workflows.
- AutoGen (by Microsoft Research) pioneered the concept of conversational multi-agent collaboration. Agents interact through multi-turn dialogue, debating and refining outputs. Microsoft has since merged AutoGen into the broader Microsoft Agent Framework, which reached Release Candidate in February 2026 with graph-based workflows and MCP support. Best suited for offline, quality-sensitive workflows where thoroughness matters more than speed.
Other notable frameworks: OpenAI Agents SDK (simplest on-ramp for GPT-based agents), Google Agent Development Kit (ADK, strong A2A and multimodal support), and Anthropic Claude Agent SDK (safety-first design with MCP support).
Also read: Langchain vs CrewAI vs AutoGen — How to Choose the Right Framework for AI agent?
2. Enterprise Platforms
Salesforce AgentForce, ServiceNow AI Agents, and UiPath’s Maestro platform represent the enterprise tier. These platforms offer pre-built agent templates, governed execution environments, and integration with existing enterprise systems. They trade flexibility for compliance, security, and out-of-the-box governance.
Open-source vs. Enterprise Platforms: How to Choose?
Your choice should be based on four factors:
- Workflow complexity
- Production maturity requirements
- Team expertise
- Your existing tech stack
Pro tip: For rapid prototyping, start with CrewAI. For production-grade stateful systems, choose LangGraph. For teams deep in the Azure ecosystem, the Microsoft Agent Framework is a natural fit.
Below is a quick overview to help you make the decision faster:
| Framework | Best For | Orchestration | Learning Curve | Protocol Support |
|---|---|---|---|---|
| LangGraph | Production workflows | Directed graph | Steep (1-2 weeks) | Community MCP |
| CrewAI | Fast prototyping | Role-based crews | Low (hours) | Native MCP + A2A |
| AutoGen/MS Agent | Conversational QA | Multi-turn chat | Medium | MCP + A2A (RC) |
| OpenAI SDK | GPT-native agents | Explicit handoffs | Lowest | Limited |
| Google ADK | Multimodal agents | Hierarchical tree | Medium | Native A2A |
Choosing the right frameworks or platform, and building an AI system sounds great but the hard facts indicate that majority of AI pilots fail. Infact, Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027. Similarly, Deloitte’s 2025 research found that while 38% of organizations are piloting agentic solutions, only 14% have production-ready systems.
So, why do AI projects fail? It is not the AI system in most cases. The answer is: production gap.
Reasons Why AI Pilots Fail
While 79% of enterprises report some level of agentic AI adoption, only 11% run agents in production. That 68-point gap is the largest deployment backlog in enterprise technology history. Below are the top 4 reasons why this gap is still there.
1. Legacy System Incompatibility
Most enterprises run on systems designed for human-triggered workflows. Agents need real-time data access, event-driven triggers, and API-first architectures. Bolting an autonomous agent onto a batch-processing ERP system designed in the 1990s creates immediate friction. Seventy percent of developers report integration problems with existing systems.
2. Missing Governance Frameworks
When an agent makes a decision, who is accountable? Most organizations have no framework for auditing, monitoring, or overriding agent decisions. Without governance, agents in customer-facing roles create liability risks. Klarna’s course correction is a prime example: aggressive automation without governance eroded customer service quality for this Swedish Fintech brand.
3. Wrong Measurement Frameworks
Most teams default to measuring agent ROI the same way they measure rule-based automation: headcount reduction and direct cost savings. This creates a problem because agentic AI delivers value differently. The real gains show up as faster decision cycles, higher accuracy in multi-step workflows, and improved output quality across departments.
When organizations apply narrow cost-cutting metrics to these broader improvements, the numbers often come back flat. This is exactly why 42% of AI projects report zero ROI. The agents may be working, but the measurement framework is not built to see the value they create. The result is a self-fulfilling prophecy: projects get cancelled for “no results” despite delivering real operational impact that no one tracked.
4. Agent Washing
Gartner identified a widespread problem across the vendor landscape: “agent washing.” This refers to vendors rebranding their existing chatbots, RPA scripts, and basic AI assistants as “agentic AI” without adding genuine autonomy, planning, or tool-use capabilities. Out of thousands of vendors making agentic claims, only about 130 deliver true agentic functionality.
This matters because teams that purchase these rebranded tools expect autonomous, goal-driven behaviour. When the product turns out to be a chatbot with a new label, the pilot underperforms. The team then concludes that “agentic AI does not work,” when the real issue is that they never deployed an agentic system in the first place. The technology gets blamed for a procurement failure.
The 12% who succeed share four attributes: pre-deployment infrastructure investment, governance documentation before deployment, baseline metrics captured before pilots, and dedicated business ownership with post-deployment accountability.
Real-world Examples of Agentic AI Across Industries
The best way to understand what agentic AI examples look like in practice is to study how companies have deployed these systems and what happened after they did. Here are five cases across different sectors, each with a distinct lesson.
1. Retail and Commerce: Shopify Agentic Storefronts
For over a decade, e-commerce ran on a simple model: drive traffic to your website, convert visitors into buyers. Shopify’s Winter ’26 Edition upended that model by introducing Agentic Storefronts. The idea is straightforward but consequential. Instead of waiting for customers to visit a store URL, Shopify now allows products to surface directly inside AI-powered conversations on ChatGPT, Perplexity, and Microsoft Copilot.
This works because Shopify restructured its product data layer for AI consumption. An agent can now discover a product, present it to the user with pricing and availability, and complete checkout entirely within the chat window. The same thinking extends to operations. Shopify’s Sidekick assistant evolved from a reactive chatbot into an agentic system that independently manages discount campaigns, updates product listings, and configures shipping rules without a merchant typing a single instruction.
2. Fintech: Klarna’s AI Customer Service Agent
Klarna partnered with OpenAI to deploy an AI agent across 23 markets and 35 languages. In its first month, the agent handled 2.3 million customer conversations. That is equivalent to the workload of 700 full-time support agents. The results were striking: a 47% improvement in customer satisfaction and over $10 million in projected annual savings.
But the story did not end there. As the agent handled a wider range of queries, customers began flagging a lack of empathy and contextual understanding in complex, emotionally charged interactions. Klarna responded by pivoting to a hybrid model, reintroducing human agents for nuanced cases while keeping AI for routine requests. This is one of the most instructive case studies in agentic deployment because it shows both the upside of scale and the cost of removing human judgment too aggressively.
3. Healthcare: AI-Powered Diagnostics Triage
Healthcare is one of the sectors where agentic AI has the highest potential and the highest stakes. The core use case is diagnostic triage: an agent reviews patient intake forms, cross-references symptoms against clinical guidelines, flags high-risk cases for immediate physician review, and routes routine cases to the appropriate department.
This matters because triage is a bottleneck in most healthcare systems. Manual review is slow, and misrouting delays treatment. Agentic triage does not replace the physician’s judgment. Instead, it ensures the physician sees the right cases first. According to Deloitte’s 2025 Emerging Technology Trends report, healthcare and life sciences is expected to be the fastest-growing segment for agentic AI deployments through 2034, precisely because the efficiency gains in clinical workflows are so significant.
4. Software Development: AI Coding Agents
GitHub Copilot Workspace and Anthropic’s Claude Code represent a new generation of coding agents, and they illustrate a clear shift in what “AI-assisted development” means. Earlier tools autocompleted lines of code. These agents do something fundamentally different. They read your entire codebase, plan a multi-file refactoring, implement changes across files, run tests, and iterate based on test failures.
The reason this qualifies as agentic (and not just smarter autocomplete) is the loop. The agent sets a goal (complete the refactor), breaks it into sub-tasks (modify file A, update imports in file B, adjust tests in file C), executes, observes failures, and replans. Developers using these tools report task completion rates up to 12x faster than traditional LLM-assisted coding, according to industry benchmarks. The speed gain comes not from faster typing but from fewer failed iterations.
5. Supply Chain: Autonomous Procurement Agents
Large manufacturers are deploying agents that manage procurement end to end: monitoring supplier prices, predicting stock-outs from demand signals, generating purchase orders, and even negotiating terms through automated email exchanges. These agents plug into ERP systems like SAP and Oracle and run continuously in the background.
The value is compounding. Each procurement cycle generates data that the agent uses to improve future predictions and negotiations. Early adopters report 40-60% reductions in procurement cycle times. The reason is not that the agent negotiates better than a human. It is that the agent never stops monitoring, never misses a price shift, and processes supplier data across hundreds of SKUs simultaneously, something a human procurement team cannot sustain at the same scale.
These use cases share a common thread: the teams that built them understood agent architectures, memory systems, and orchestration frameworks at a technical level.
Now that we know the technical aspects of what is agentic AI, types and usecases, let’s understand what the immediate future looks like before we wrap up.
The Future of Agentic AI: What Comes Next
Three developments will define the next phase of agentic AI.
1. Interoperability Protocols Become Standard
Right now, most agents are locked into the ecosystem they were built in. A LangGraph agent cannot natively talk to a CrewAI agent, and neither can easily connect to an enterprise tool without custom integration code. This fragmentation slows adoption because every new tool or data source requires bespoke plumbing.
Two protocols are changing that. Anthropic’s Model Context Protocol (MCP) standardises how agents connect to external tools, databases, and APIs, turning what used to be custom integration into plug-and-play connectivity. Google’s Agent-to-Agent Protocol (A2A), launched in April 2025, goes a step further by defining how agents from different vendors discover and invoke each other through standardised task interfaces. Together, they are doing for agentic AI what HTTP did for the web: creating a shared language that lets independently built systems work together. As these protocols mature, the cost and complexity of deploying multi-tool, multi-vendor agent systems will drop significantly.
2. Agentic Commerce Goes Mainstream
Commerce is one of the first sectors where agentic AI is moving from experimental to infrastructural. McKinsey estimates that agentic commerce could influence $3-5 trillion in global retail spend by 2030. That projection is grounded in real infrastructure shifts happening today.
Shopify now enables in-chat checkout via AI agents on ChatGPT, Perplexity, and Copilot. This works because Shopify restructured its product catalogue for AI-native discovery. Klarna took a complementary approach by launching its Agentic Product Protocol, an open standard that gives AI agents structured access to over 100 million products and 400 million price points across 12 markets. On the payments side, Visa introduced a Trusted Agent Protocol for verifying AI agents in transactions, and Mastercard’s Agent Pay enables cryptographically signed mandates for agent-led purchases. The pattern is clear: discovery, evaluation, and transaction are all being rewired for agent-first execution. When the companies that control these three layers all move in the same direction, the shift becomes structural, not speculative.
3. The Learning Agent in AI Moves to Production
Most agents deployed today are static. They follow a fixed reasoning pattern, and if you want them to improve, you manually update prompts or retrain components. The concept of a learning agent in AI, one that improves its own performance based on experience, has been a research goal for years. It is now beginning to show up in production.
Google DeepMind’s SIMA agent demonstrated this capability by learning over 600 skills across 9 game engines, adapting its strategy based on outcomes rather than relying on pre-programmed instructions. Enterprise applications are following the same principle. Customer support agents are learning from past ticket resolutions to improve future triage accuracy. Procurement agents are learning from negotiation outcomes to secure better terms over time.