AI Agents: Current Landscape and Use Cases

AI agents are a new frontier. Unlike chatbots that respond to questions, agents autonomously plan and execute multi-step workflows. For product teams, agents promise automation at scale. But they're still experimental. This guide covers what they are, real use cases, and when to build them.

What Are AI Agents?

An AI agent is an LLM equipped with:

• Tools: APIs the agent can call (search, database queries, email, code execution).
• Memory: Context from past interactions or long-term data stores.
• Planning: Logic to decide what step comes next (e.g., "I need to research X, then calculate Y, then present findings").

Example: a research agent that takes a topic, searches the web, reads articles, summarizes findings, and drafts a report. Each step is one or more tool calls orchestrated by the LLM.

Current Use Cases

Coding Agents (GitHub Copilot X, Claude Dev): Write tests, refactor code, debug. Productivity gain is real; developers are 30-40% faster with agentic assistance.

Customer Support Automation: Agents resolve tickets autonomously: retrieve customer history, search knowledge base, draft response, escalate if needed.

Research and Analysis: Agents gather data from multiple sources, synthesize insights, generate reports.

Sales Automation: Agents qualify leads, send follow-ups, schedule meetings.

For Indian companies, emerging use cases:

• KYC Verification: Agents verify identity documents, cross-check against registries, flag anomalies.
• Document Processing: Extract info from invoices, contracts, compliance docs. Common in fintech and legal.
• Multilingual Support: Agents handle regional language queries, translate, escalate if needed.

Limitations and Challenges

Agents aren't magic. Key limitations:

• Cost: Agents make many LLM calls. A complex workflow might cost $1-10 per execution. At scale, this adds up.
• Reliability: Agents can get stuck in loops, make wrong decisions, or hallucinate. They need guardrails and human oversight.
• Hallucination Accumulation: Each agent step can introduce errors. Errors compound across steps.
• Latency: Multi-step workflows take time. Real-time requirements are hard.

Key Takeaways

• Agents are useful for complex, multi-step workflows that require autonomy and tool use.
• Current best use cases: coding assistance, customer support, research, document processing.
• Always include human-in-the-loop validation, especially for high-stakes decisions.
• Monitor cost closely; agents can be expensive at scale.
• For Indian companies: KYC, document processing, and multilingual support are ripe for agentic automation.