Agentic AI, Part 1

@SudhirNakka07|September 6, 2025 (7d ago)5 views

Introduction

Agentic AI refers to intelligent systems that plan, decide, and act to achieve user-defined goals. Unlike simple automations or purely generative systems that react to prompts, agentic systems operate proactively across multiple steps, integrating reasoning, planning, and tool use.

Human + Automation vs Agentic AI

Let’s walk through a relatable task and compare the flows.

Task: Reach home for Dad’s birthday.

Human + Automation Flow:

[Human] Find destination (city + address).
[Human + Automation] Find birthday (calendar, email, messages).
[Human + Automation] Find arrival time (calendar, constraints).
[Human + Automation] Plan route (maps, transit, flight search).
[Human + Automation] Fetch flight and price options.
[Human] Review options, select, reserve.
[Human + Automation] Create/update calendar events (birthday, cabs, flight, home).
[Human + Automation] Add reminder to buy a gift.
[Human] Purchase gift and travel.

Agentic AI Flow:

[AgenticAI] Determine destination (city + address).
[AgenticAI] Retrieve birthday (calendar, email, messages).
[AgenticAI] Determine arrival time (calendar, constraints).
[AgenticAI] Plan route (maps, transit, flight search).
[AgenticAI] Fetch flight and price options.
[AgenticAI + Human] Present options, get selection, make reservations.
[AgenticAI] Create/update calendar events (birthday, cabs, flight, home).
[AgenticAI] Add reminder to buy a gift.
[Human] Purchase gift and travel.

The Agentic AI Cycle

Agentic AI operates in a continuous loop of perception, reasoning, and action. User objectives are translated into executable plans.

Context Perception: Gather and process data from databases, user input, APIs, sensors, etc., to understand the current context.
Reasoning and Planning: An LLM acts as the “brain,” decomposing the goal into sub-tasks and sequencing a plan.
Action: Agents execute the plan by calling tools and systems (APIs, MCP servers, code, browser automation, etc.).
Learning and Reflection: Evaluate outcomes, refine strategies via feedback loops, and improve over time.

Agentic AI vs Other AI Systems

Feature	Agentic AI	Generative AI	Traditional AI
Primary Function	Autonomous, goal-oriented action and decision-making.	Create new content (text, code, images) from prompts.	Automate repetitive, specific tasks via predefined rules.
Autonomy	High — initiates and completes tasks with minimal supervision.	Low — reactive to user prompts.	Low — depends on fixed algorithms and triggers.
Workflow	Dynamic and adaptive; can course-correct.	Reactive and stateless; one request at a time.	Static and rule-based; brittle outside scoped parameters.
Planning & Reasoning	Multi-step planning, decomposition, and decision-making.	Limited explicit planning; mainly prompt-to-output.	No planning; executes predefined logic.
Tool Use	Integrates tools/APIs, external services, and automation to act.	Optional tool use via plugins/functions, typically within a single request.	Fixed integrations; scripted automations.
Memory/State	Maintains working memory and longer-term context for tasks.	Mostly stateless per request; limited session memory.	State handled via application code and databases; not adaptive.
Learning & Feedback	Reflects on outcomes; can self-correct via feedback loops.	No built-in reflection; improvements via retraining or prompt tuning.	No learning; behavior changes only via code/config updates.
Human-in-the-loop	Human sets goals and approves key decisions when needed.	Human crafts prompts and reviews outputs.	Human designs rules; minimal runtime interaction.
Typical Outputs	Completed tasks, updates to systems, and decisions taken.	Text, images, code, audio generated from prompts.	Deterministic actions within a narrow scope.
Example	Handles a support ticket end-to-end (verify, refund, update CRM).	Writes a marketing email or generates an image.	Scripted chatbot or a simple spam filter.

Is it all fun and glory?

Real‑world Use Cases

The adaptive, autonomous nature of agentic AI enables it across industries to handle complex, multi-step processes:

Customer Service: Resolve complex inquiries end-to-end, from troubleshooting and assisting with purchases to issuing refunds.
Financial Services: Detect and block fraud in real time by analyzing transaction streams.
IT Service Management (ITSM): Resolve low-level tickets (password resets, access provisioning) using authorization rules.
Software Development: Generate code, run tests, triage bugs, and submit PRs.
Supply Chain: Reroute shipments, adjust inventory, and place restock orders dynamically.
Healthcare: Assist with diagnostics from medical records and automate scheduling/administrative tasks.

Risks and Challenges

Agentic AI remains early; risks mirror broader AI concerns and introduce new ones:

Privacy: Collection and use of personal data to provide services and improve performance.
Security: Vulnerable to malicious use or compromise.
Compliance: May not align with regulations without careful guardrails.
Efficiency/Cost: Compute vs human trade-offs can make some tasks costly.
Ecosystem: Integration with existing systems is non-trivial; MCP interfaces help, but gaps remain.
Accountability: Assigning responsibility is hard; opaque reasoning hampers audits.
Security Vulnerabilities: Memory poisoning, goal manipulation, or misuse of tools/data.
Unintended Consequences: Poorly defined goals can lead to undesirable optimizations (e.g., risky trading).
Data Bias: Biased training can produce discriminatory decisions.
Integration Complexity: Connecting to legacy systems can be resource-intensive.
Over‑reliance: Excessive trust reduces human oversight and error detection.

This is Part 1. In future parts, we will explore agent architectures, memory, tool interfaces (incl. MCP), and evaluation strategies.