Solving Real-Life Problems with Agentic Code

Agentic AI represents a leap beyond traditional automation, where systems perceive, reason, act, and learn autonomously to tackle complex, multi-step real-world problems. Unlike rule-based tools, agentic code orchestrates workflows, adapts to changes, and integrates with external systems… powering solutions in industries from healthcare to software engineering.

Core Capabilities of Agentic Code

Agentic code excels in environments with fragmented data, real-time decisions, and evolving conditions. Key strengths include:

Multi-step orchestration: Breaking down goals into actions, like app development or incident response.
Tool integration: Interfacing with APIs, databases, and external services for autonomous execution.
Adaptation and learning: Updating processes based on new data, such as regulatory changes or user behavior.
Collaboration: Multi-agent systems where specialized agents handle subtasks, like data collection and risk assessment.

Real-Life Examples Across Industries

Software Engineering: Building Apps in Minutes

A developer used OpenAI’s Operator and Replit’s AI Agent to build an entire app in 90 minutes. The agents exchanged credentials, ran tests, and handled multi-file coding autonomously. Similarly, Cursor’s Composer generates a full Tic Tac Toe game from one prompt: “Generate an HTML, CSS, and JavaScript Tic Tac Toe game for 2 players.” It manages files, executes commands, and infers context.

Healthcare: Proactive Patient Care

For a large U.S. health provider, agentic AI integrated EMR, insurance, and scheduling systems. Patients receive proactive care reminders based on history, yielding a 30% drop in support volume and 25% boost in satisfaction.

Customer Service: End-to-End Resolutions

In telecom, AAVA-powered agents reduced case resolution times by 45% via context-aware virtual assistants that integrate enterprise data. Ada AI Agent handles customer calls with natural language processing for inquiries and tickets.

Cybersecurity: Autonomous Threat Response

Google’s SOC Manager uses sub-agents for incident response: ingesting telemetry, enriching alerts with IOCs, and executing containment via automated runbooks. Another system generates Sigma rules for threat hunting, like Kerberoasting detection, via collaborative agents querying MITRE ATT&CK.

Insurance and E-Commerce: Streamlined Operations

Akira AI‘s multi-agent system automates underwriting: one agent collects data, another evaluates risk, a third detects fraud, and pricing follows.

For shopping, agents like MultiOn Agent Q handle long-horizon tasks, such as booking flights or ordering items under constraints (e.g., “Buy a boho-style throw pillow cover under $30”).

Building Agentic Code: Practical Steps

Start with frameworks like LangChain for tool use, scaling to advanced setups with memory and planning (e.g., Auto-GPT). Challenges include managing task complexity, decision standardization, and API interfacing… but real-world wins like 90-minute app builds prove the payoff.

Example prompt for agentic coding:

“Generate a Vendor self service dashboard in HTML/CSS/JS across multiple files, test it, and deploy.”

Industries with complex workflows… banking, logistics, HR… benefit most, from code modernization to inventory forecasting.

Future Outlook

Agentic code will drive self-evolving workflows, edge AI, and innovation engines, setting benchmarks in an AI-powered economy. Early adopters in software engineering and compliance are already transforming operations.

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Jitendra Chaudhary

Jitendra Chaudhary is an IT veteran with over 28 years of experience architecting the bridge between traditional enterprise systems and the future of intelligence... From leading complex ERP implementations to developing agentic AI workflows, Jitendra has spent three decades simplifying the complex...

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