Demystifying AI Agents

‍For over a decade, enterprise teams chased the vision of business intelligence delivering "decision-making at the speed of thought." But the reality fell short—clunky data pipelines, abandoned dashboards, and analysts buried in manual prep work became the norm.

By investing deeply in AI agents, companies aim to bridge the persistent gap between raw data and real-world decisions. These agents operate quietly in the background, empowering business users to ask natural questions and get meaningful answers instantly. The impact could be transformative: freeing analysts from routine tasks and reshaping their roles into strategic drivers of innovation across industries.

AI agents are one of the hottest topics in tech right now. But what exactly are they? Are they just a repackaging of existing AI concepts, or is there something fundamentally new happening?

In this blog, we’ll break down:

• What AI agents are
• How they evolved from LLMs and GenAI
• Current use cases (hype vs. reality)
• The role of protocols like MCP and A2A
• How Incorta is enabling AI agents
• The challenges of agent evaluation

What Is an AI Agent?

AI agents are defined as software systems that use AI to pursue goals and complete tasks. They show reasoning, planning, memory, and a level of autonomy to make decisions, learn, and adapt.‍

Key Characteristics of AI agents include: 

1. Autonomy – They can take actions without constant human input.
2. Reasoning & Planning – They break down tasks logically.
3. Memory – They retain context from past interactions.
4. Tool Use – They interact with APIs, databases, and other software.

‍For example:

• A bot follows strict "if X, then Y" rules.
• An AI assistant answers questions reactively.
• An AI agent can plan a full project, gather data, and execute tasks with minimal human input.

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The Evolution: From LLMs to Agentic AI

You begin with an LLM and a prompt.
Next, you apply RAG (Retrieval-Augmented Generation) to pull relevant facts from data sources—via search—and insert them into the prompt.
Then, using a capability called function calling, the LLM can decide which APIs to invoke, generate the correct arguments, and execute them.
With MCP (Model Context Protocol), you standardize how models connect to tools, data, and external systems—making these interactions more modular, reusable, and interoperable.
On top of that, you introduce a reasoning loop: checking whether the user’s intent is understood, validating the plan, verifying tool choices, and reviewing results before returning a response.
Finally, you scale this up to multiple agents—each specialized for specific tools, domains, or processes—that collaborate with one another.

Each of these steps could be a full college course in itself, full of techniques, trade-offs, and subtleties. Every new variation or pattern represents a developer or researcher’s attempt to produce higher-quality results—more reliably, more efficiently, and at lower cost.

To summarize:

1. LLM-Powered Chatbots (2022-2023)
   
   • Basic question-answering with hallucinations.
   • Limited by lack of grounding in real-world data.
2. RAG (Retrieval-Augmented Generation) Chatbots (2023-2024)
   
   • Added context via structured/unstructured data (e.g., Incorta SQL engine, Incorta RAG for Unstructured data).
   • Better answers but still reactive.
3. AI Agents (2025 & Beyond)
   
   • Reasoning loop: You describe a goal in plain English, and the agent plans & executes.
   • Tool integration: Uses APIs, MCPs, databases, and workflows to take action.

What Enabled This Shift?

• Chain-of-Thought (CoT) Models – LLMs like GPT-5 now "think" before responding.
• Multi-Modal AI – Agents can process text, voice, images, and video.
• Standardized Protocols (MCP, A2A) – Tools, other agents, and systems can now interoperate seamlessly.

Where Are AI Agents Being Used Today?

According to Gartner, the top deployments are (In order):

1. Research & Summarization  – Automating literature reviews, data analysis.
2. Personal Productivity – AI notetakers, scheduling assistants.
3. Customer Service – Automated ticketing, call center agents.
4. Code Generation – GitHub Copilot (Spark), Replit, OpenAI’s Codex, Cursor, Claude Code, Lovable, etc.
5. Data Transformation – Incorta's Data Engineering Agent.
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Real-World Examples of AI Agents

• Klarna’s AI Agent – Handled 2.3M customer service chats (equivalent to 700 full-time agents).
• Duolingo’s AI Tutors – Replaced some human contractors.
• Replit – AI coding agents and software deployment.

MCP & A2A: The Protocols Powering Agents

1. MCP (Model Context Protocol)

• Standardizes how LLMs interact with tools (APIs, databases).
• Example: Incorta exposes its query engine as an MCP-compatible tool to query massive enterprise data from complex transactional source systems.

2. A2A (Agent-to-Agent Protocol)

• Allows different AI agents to communicate.
• Each agent has an "agent card" (identity, authentication, capabilities).

How Incorta is enabling AI agents

Unlike traditional data warehouses that provide static snapshots, Incorta delivers real-time access to all your enterprise data. Your AI and ML models can:

• Train on live, current data instead of outdated historical information
• Adapt to changing business conditions in real-time
• Learn from actual workflows and processes as they happen
• Provide insights based on the most current business state

Unified Data Foundation

Incorta eliminates the data silos that cripple AI initiatives by creating a unified data layer that:

• Connects directly to all your source systems without complex ETL processes
• Maintains data relationships and context that AI models need
• Provides a single source of truth that AI can reliably learn from
• Enables AI to understand your complete business picture, not just fragments

The Biggest Challenge: Agent Evaluation

Agents are stochastic (non-deterministic), making them hard to test.

Key Evaluation Metrics:

Capability – Can it do the task?
Reliability – Can it do it consistently?

The Human-in-the-Loop Problem

• No fully autonomous agents exist yet. Mostly workflow agents.
• Agents still need human validation (e.g., "Is this the right approach?").

The Next Frontier: Agent Memory

Current agents forget past interactions. Research now is focusing on:

• Semantic memory (facts)
• Episodic memory (past experiences)
• Procedural memory (learned skills)

Final Thoughts: Hype vs. Reality?

• The Hype: "AI agents will replace all jobs!"
• The Reality: Agents are powerful, but they do still need human in the loop (as of now)

‍What’s Next?

• More specialized agents (coding, data, research).
• Better memory & reliability solutions.
• Standardized agent ecosystems (A2A massive adoption).

What do you think? Are AI agents overhyped, or is this the next big shift in AI? Write in the comments. 

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