Agentic AI – Primer

Types of Agents

Just like microservices, agents can be specialized for different roles. A well-designed Agentic AI solution involves layering these agents effectively.

1. Technical Agents

Handle system-level tasks. Examples:

• API Agent: Calls APIs and returns results
• RAG Agent: Reads documents, extracts information using Retrieval-Augmented Generation
• DB Agent: Reads/writes to databases
• LLM Agent: Interprets user queries and invokes internal tools

2. Functional Agents

Focus on specific user-facing tasks:

• Weather Agent: Retrieves and analyzes weather data using a Technical API Agent
• Research Agent: Gathers data from multiple sources and summarizes it

You can sometimes merge functional and technical roles for smaller solutions.

3. Operational Agents

Ensure the system behaves securely and efficiently. Examples:

• Security Agent: Prevents data leakage between agents
• Pricing Agent: Tracks API usage and costs to stay within budget

4. Augmentation Agents

These agents manage memory and context. Examples:

• Track prior interactions
• Cache results and user preferences
• Enable forecasting and proactive responses

Example: If a user checks their banking details, the agent remembers recent transactions and suggests helpful actions based on context.

Context Management – The Secret Sauce! Context enables a human-like, continuous experience. A good system should:

• Remember user preferences
• Recall past function calls and results
• Proactively respond using learned or retrieved data

This is where Lang Chain’s memory and chaining mechanisms shine ( Which we will discuss in the example)

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Coordination Structures

There are multiple ways to structure your agentic workflows:

1. Linear Pattern

A step-by-step pipeline:

2. Waterfall Pattern

A central Planning Agent acts as a master orchestrator, coordinating sub-agents in sequence.

3. Hub-and-Spoke Pattern

Agents communicate freely among themselves like a network of peers.

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Error Handling

Robust error handling is a must:

• Validate user input before acting on it
• Sanitize and check agent output before forwarding it
• Handle API failures gracefully
• Add fallback strategies (e.g., retrying with alternate methods)

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Using LLMs as Agents

Instead of designing the architecture and flow like this :

  User → AI Agent

Try:

  User → LLM Agent → AI Agents

Benefits of using LLM ( ChatGPT, Gemini…) in the agentic AI framework:

• Understand natural language better
• Translate user intent to agent tasks
• Coordinate multiple agents
• Add reasoning and foresight

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Operational Best Practices

To build resilient, production-grade AI agent systems:

• Monitor agent health and usage
• Track API limits, costs, and performance
• Handle agent failure without halting the entire system
• Maintain consistent state across all agent interactions
• Enable real-time alerts for failures or high latency

Debugging Tip:

Include a debug mode in each agent that prints input/output and memory state for easy tracing during development.

Building Resilient Agentic AI:

Resilience ensures your solution keeps working under stress:
– In most cases you will be using Cloud solution for developing complex Agentic solution. Ensure cloud agents auto-replicate across availability zones in case of regional outages.
– Graceful Failures: If one agent fails, route the flow to alternatives instead of crashing
– Standard Protocols: Use standard agent communication protocols (e.g., LangChain or OpenAI tool specs)