AI Agent

1 What is AI Agent?........................................................................................................................ 2

1.1 Real-world use cases............................................................................................................. 2

1.2 Type of AI Agents.................................................................................................................. 3

2 Step to build an AI Agent............................................................................................................. 4

2.1 10 Questions to ask before consider an AI Agent................................................................. 5

3 AI Agent Architecture.................................................................................................................. 6

3.1 Layer 1: LLM (Foundation Layer): OpenAI, Gemini, LLaMA, Claude.............................. 6

3.2 Layer 2: Knowledge Base(KB), Vector Database: PostgreSQL, Pinecone, Redis............... 7

3.3 Layer 3: RAG (Retrieval-Augmented-Generation): Langchain, Llamaindex,..................... 7

3.4 Layer 4: Safety & Ethics: Azure Content Filter, OpenAI Moderation API.......................... 7

3.5 Layer 5: Operational Logic & Autonomy: AutoGen, CrewAI, LangGraph, Autogen.......... 7

3.6 Layer 6: Interation Interface: OpenAI Assistant API, Streamlit, Gradio.............................. 7

3.7 Layer 7: External Integrations: Zapier, Make.com, LangChain Agents, n8n....................... 8

3.8 Layer 8: Governance & Observability.................................................................................. 8

4 Top Frameworks in Python.......................................................................................................... 8

5 Evaluation, Problem, and Solution............................................................................................... 9

5.1 Metrics for Evaluating AI Agent........................................................................................... 9

5.2 Development Issues & Fixes............................................................................................... 10

5.3 LLM Issues & Fixes............................................................................................................ 10

5.4 Production Issues & Fixes................................................................................................... 11

1 What is AI Agent?

An AI agent is a system that can perceive its environment, process information, make decisions, and take actions to achieve a specific goal. Think of it as an autonomous entity that can perform tasks on your behalf.

1.1 Real-world use cases

- Clinical History Search Engine

- Predictive Maintenance Agent

- Protocol Summarizer

- IOQ/IOK Documents Extraction

- Route Optimization System

- Marketing Campaign Agent

- SOAP Notes Generator

- Inventory Management Assistant

- Anti-Fraud Agent

1.2 Type of AI Agents

AI Agent Types Comparison Table

Agent Type	Core Capability	Autonomy Level	Typical Use Cases	Pros	Cons
Fixed Automation	Performs pre-defined tasks with rigid logic	🔹 Minimal	Manufacturing, routine workflows	Fast, predictable, low-cost	No adaptability or learning
LLM-Enhanced	Uses large language models for flexible task execution	🔸 Moderate	Email summarization, content generation	Natural language understanding, broader versatility	Limited reasoning, no external memory
ReAct / ReAct + RAG	Combines reasoning & acting (ReAct) + retrieval from DBs	🔸 Moderate–High	Q&A, research tasks, code generation	Context-aware, info-rich, iterative reasoning	Can be slow; needs strong retrieval configuration
Tool-Enhanced	Accesses external tools, APIs, or databases	🔸 Moderate–High	Web scraping, automation, analytics	Scales real-world utility, can fetch live data	Tool calls may fail; more complexity to manage
Self-Reflecting	Evaluates own actions and learns from errors	🔸 High	Strategy agents, creative writing, multi-step logic	Improves iteratively; useful for longer workflows	Requires evaluation loops; can get stuck in reflection
Environment Controllers	Interacts with real or simulated environments	🔹 High	Games, robotics, smart homes	Real-time decisions; supports feedback from surroundings	Needs environment simulators; harder to test
Self-Learning	Learns from new data or experiences without retraining	🔺 Very High	Predictive agents, personalization systems	Adaptive, data-driven improvements	Hard to control outcomes; risks of model drift

🔹 = Basic autonomy
🔸 = Intermediate autonomy
🔺 = Advanced autonomy

2 Step to build an AI Agent

1) Define the Goal: Clearly state what you want the agent to accomplish. This will guide the entire development process.

2) Choose the Right Model: Select the appropriate AI model for the task. This could be a large language model (LLM) like GPT-4, a smaller, more specialized model, or a combination of models.

3) Gather Data: Collect and prepare the data the agent will need to learn and make decisions. This could include documents, conversation histories, or data from external APIs.

4) Train the Model (if necessary): For learning agents, you'll need to train the model on your data to improve its performance on the specific task.

5) Develop the Orchestration Layer: This is the logic that governs how the agent processes information, plans, and executes actions. It connects the model to the tools and data it needs.

6) Integrate Tools: Provide the agent with the necessary tools to interact with the outside world. This could include web search, file access, or connections to other software.

7) Deploy and Monitor: Once built, deploy the agent and continuously monitor its performance to identify areas for improvement.

2.1 10 Questions to ask before consider an AI Agent

1) What is the complexity of the task?

2) How often does the task occur?

3) What is the expected volume of data or queries?

4) Does the task require adaptability?

5) Can the task benefit from learning & evolving over time?

6) What level of accuracy is required?

7) Is human expertise or emotional intelligence essential?

8) What are the privacy and security implications?

9) What are the regulatory and compliance requirements?

10) What is the cost-benefit analysis?

3 AI Agent Architecture

3.1 Layer 1: LLM (Foundation Layer): OpenAI, Gemini, LLaMA, Claude

Purpose: Core reasoning, generation, planning.

Tools: GPT-4 (OpenAI), Claude, Cohere, Mistral, Gemini, LLaMA

3.2 Layer 2: Knowledge Base(KB), Vector Database: PostgreSQL, Pinecone, Redis

Purpose: Structured/unstructured context for better responses.

Tools: Chroma, Weaviate, Pinecone, Redis, PostgreSQL

3.3 Layer 3: RAG (Retrieval-Augmented-Generation): Langchain, Llamaindex,

Purpose: Retrieves relevant data from docs/dbs before responding.

Tools: LangChain RAG, LlamaIndex, Haystack, Unstructured.io

3.4 Layer 4: Safety & Ethics: Azure Content Filter, OpenAI Moderation API

Purpose: Adds guardrails to prevent bias, harm, or inappropriate behavior.

Tools: Azure Content Filter, OpenAI Moderation API, GuardrailsAI, Rebuff

3.5 Layer 5: Operational Logic & Autonomy: AutoGen, CrewAI, LangGraph, Autogen

Purpose: Autonomous task planning, decision-making, and execution.

Tools: AutoGen, CrewAI, MetaGPT, LangGraph, Autogen Studio

3.6 Layer 6: Interation Interface: OpenAI Assistant API, Streamlit, Gradio

Purpose: Connects users or systems with the agent (multimodal input/tool use).

Tools: OpenAI Assistant API, Streamlit, Gradio, LangChain Tools, Function Calling

3.7 Layer 7: External Integrations: Zapier, Make.com, LangChain Agents, n8n

Purpose: Access tools/services like APIs, CRMs, browsers, scrapers.

Tools: Browserless, Zapier, Make.com, Serper API, LangChain Agents, n8n

3.8 Layer 8: Governance & Observability

Purpose: Tracks actions, logging, roles, ethics, boundaries.

Tools: Helicone, Promptlayer, Trulens, LangSmith, WandB

4 Top Frameworks in Python

AI Agent Frameworks Comparison Table

Framework	Core Functionality	Best For	Strengths	Limitations
LangChain	Modular orchestration of LLM agents & tools	Custom agent pipelines, chains	Rich ecosystem, tool integration, community support	Can be complex to configure for large workflows
DeepLake	Data lake optimized for ML and embeddings	Storing embeddings, training data	Fast vector search, scalable, versioned datasets	Not focused on agent logic or orchestration
AutoGPT	Autonomous agent that breaks down tasks recursively	Task automation with minimal setup	Easy to use, popular, autonomous prompt chaining	Less control, fragile tool usage, lacks granularity
OpenAI Swarm	Multi-agent coordination via OpenAI LLMs	Parallel agent tasks, delegation	Powerful for collaboration, scalable task resolution	Limited documentation, experimental features
Prodigy	Active learning annotation tool	Human-in-the-loop training workflows	Real-time annotation, supports NLP and computer vision	Focused on dataset labeling, not agent execution
LlamaIndex	Indexing and querying for agent memory & RAG	Long-term memory, document retrieval	Fast context access, structured doc ingestion	Not an agent runtime, needs pairing with orchestration
AutoGen	Multi-agent workflow engine from Microsoft	Role-based agents and tool usage	Chat-based coordination, extensible, open source	Requires planning; limited examples for complex agents
Meta AgentKit	Metamodel control and orchestration	Agent switching, multi-model tasks	Model flexibility, advanced routing	Early-stage, may need custom tuning for workflows
Vertex AI Agent Builder	Google’s agent creation platform in Vertex AI	Cloud-native agent deployment	Seamless GCP integration, low-code options	Tied to Google Cloud, less flexible than open frameworks