Beyond RAG: The Agentic Search Stack for Truly Intelligent Enterprise Chatbots

March 1, 2025•22 min read•Updated for 2025

Key Takeaways:

Agentic search enables chatbots to perform actions, not just answer questions
New frameworks like LangGraph and CrewAI make it easier to build agentic systems
Agentic search can handle complex, multi-step workflows that RAG alone cannot
Implementation requires careful planning around tooling, memory, and orchestration

The limitations of traditional Retrieval-Augmented Generation (RAG) systems are becoming increasingly apparent as enterprises demand more capable AI assistants. While RAG excels at retrieving and reformulating information, it falls short when it comes to performing actions, reasoning through complex problems, or adapting to new situations. Enter agentic search - a new paradigm that combines the strengths of RAG with the power of autonomous agents that can plan, reason, and take action.

The Evolution from RAG to Agentic Search

Traditional Search (Pre-2023)

Keyword-based retrieval with limited understanding of context or intent. Systems like Elasticsearch and Solr dominated this era, providing fast but often imprecise results.

RAG Systems (2023-2024)

Combined retrieval with large language models to provide more accurate, context-aware responses. Marked a significant improvement but still limited to information retrieval and reformulation.

Agentic Search (2024-2025)

AI agents that can understand intent, plan actions, use tools, and reason through complex problems. These systems don't just retrieve information - they can perform tasks and solve problems.

Note: While RAG remains a critical component, agentic search represents a fundamental shift in how we think about AI assistants - from information retrieval systems to autonomous problem solvers.

RAG vs. Agentic Search: Key Differences

Aspect	RAG	Agentic Search	Advantage
Query Understanding	Keyword and semantic matching	Contextual understanding with reasoning	Agents understand intent and context better
Response Generation	Direct retrieval and reformulation	Dynamic response construction with reasoning	Agents can explain their reasoning
Multi-step Tasks	Limited to single retrieval step	Can break down and solve multi-step problems	Agents can handle complex workflows
Tool Usage	No tool usage	Can use external tools and APIs	Agents can perform actions
Learning & Adaptation	Static knowledge base	Can learn from interactions	Agents improve over time

The Agentic Search Stack: Key Components

1. Core Agent Framework

The foundation that defines how agents operate, make decisions, and interact with other components.

Example: LangGraph for complex agent workflows, CrewAI for role-based agents.

2. Tool Integration

Enables agents to interact with external systems, APIs, and services to perform actions.

Example: Function calling, API tools, code execution environments.

3. Memory Systems

Short-term and long-term memory to maintain context and learn from interactions.

Example: Vector databases, SQL, knowledge graphs.

4. Planning & Reasoning

Capabilities for breaking down complex tasks, generating plans, and reasoning through problems.

Example: Chain-of-thought, tree-of-thought, ReAct framework.

5. Evaluation & Monitoring

Tools to assess performance, track metrics, and ensure reliability in production.

Example: LangSmith, Arize, custom evaluation frameworks.

6. Orchestration

Manages the flow of information and control between different agents and components.

Example: LangGraph, Temporal, Airflow for agents.

Top Agentic Search Tools in 2025

LangGraph

Agent Orchestration

Framework for building stateful, multi-actor applications with LLMs

Visit LangGraph

Key Features

Stateful agent workflows
Multi-agent collaboration
Built-in memory and tools
Visual debugging

Pricing

Open-source (Apache 2.0)

Best For

Complex, multi-step agent systems

CrewAI

Multi-Agent Framework

Framework for orchestrating role-playing, autonomous AI agents

Visit CrewAI

Key Features

Role-based agent definitions
Task delegation
Built-in tools
Process automation

Pricing

Open-source (MIT)

Best For

Business process automation

AutoGen

Multi-Agent Framework

Microsoft's framework for creating multi-agent conversations

Visit AutoGen

Key Features

Customizable agents
Seamless human participation
Built-in agent capabilities
Web UI for monitoring

Pricing

Open-source (MIT)

Best For

Research and enterprise applications

Semantic Kernel

AI Orchestration

Microsoft's lightweight SDK for integrating LLMs

Visit Semantic Kernel

Key Features

Planner for complex tasks
Memory and context management
Extensible architecture
.NET and Python support

Pricing

Open-source (MIT)

Best For

Enterprise AI applications

LangChain

LLM Application Framework

Framework for developing applications with LLMs

Visit LangChain

Key Features

Chains and agents
Document loaders
Memory management
Tool integration

Pricing

Open-source (MIT)

Best For

Rapid LLM application development

Implementation Roadmap

Transitioning from RAG to agentic search is a journey. Here's a phased approach to implementation:

1. Foundation

Set up basic RAG pipeline
Implement document processing
Create vector database
Build simple retrieval system

Timeline: 2-4 weeks

2. Enhancement

Add query understanding
Implement response generation
Add basic tool usage
Set up evaluation metrics

Timeline: 4-6 weeks

3. Agentic

Implement agent framework
Add multi-step reasoning
Integrate external tools
Set up feedback loop

Timeline: 6-8 weeks

4. Optimization

Performance tuning
Cost optimization
Scalability improvements
Advanced monitoring

Timeline: Ongoing

Real-World Use Cases

Enterprise Customer Support

Agentic systems can handle complex customer service scenarios that require accessing multiple systems, making decisions, and performing actions like processing returns or scheduling appointments.

Multi-step workflowsSystem integrationAction execution

Research & Analysis

Agents can conduct comprehensive research by gathering information from multiple sources, analyzing data, and synthesizing findings into actionable insights.

Information gatheringData analysisInsight generation

Business Process Automation

Automating complex business processes that involve multiple steps, decisions, and system interactions, such as employee onboarding or invoice processing.

Workflow automationDecision makingSystem integration

Personal AI Assistants

Advanced personal assistants that can manage schedules, make reservations, handle communications, and perform tasks across multiple applications.

Task managementCalendar integrationMulti-app workflow

Challenges and Considerations

Important: While agentic search offers significant advantages, it also introduces new challenges that must be carefully managed.

1. Complexity Management

Agentic systems are inherently more complex than traditional RAG implementations. This complexity can lead to:

Harder debugging and testing
Increased development and maintenance costs
More potential points of failure

2. Cost Considerations

Agentic systems typically have higher operational costs due to:

Longer context windows
More API calls for tool usage
Increased compute requirements

3. Reliability & Safety

Autonomous agents can take unexpected actions, leading to:

Unintended consequences from tool usage
Potential security vulnerabilities
Regulatory and compliance risks

4. Evaluation & Monitoring

Assessing agent performance is challenging because:

Traditional metrics may not capture success
Agents can take many paths to a solution
Human evaluation is often required

Mitigation Strategies

Start small: Begin with limited-scope agents and gradually expand capabilities
Implement guardrails: Set clear boundaries on agent actions and tool usage
Human-in-the-loop: Keep humans involved for critical decisions and oversight
Robust testing: Develop comprehensive testing frameworks for agent behavior
Cost monitoring: Implement usage tracking and cost controls

Frequently Asked Questions

Is agentic search right for my use case?

Agentic search is particularly valuable when:

Your tasks require multi-step reasoning or problem-solving
You need to integrate with multiple systems or tools
Your users need help with complex, open-ended questions
You want your system to take actions, not just provide information

For simpler Q&A or document retrieval, traditional RAG might be more appropriate and cost-effective.

How much does it cost to implement agentic search?

Costs can vary widely based on your requirements:

Basic implementation: $0-500/month (open-source tools, small scale)
Mid-range deployment: $500-5,000/month (premium features, moderate usage)
Enterprise solution: $5,000+/month (custom development, high volume)

The main cost drivers are API usage (for LLM calls), compute resources, and development time.

What skills does my team need to implement agentic search?

A successful implementation typically requires:

Machine Learning/LLM expertise: Understanding of language models and prompt engineering
Software engineering: Strong programming skills (Python, JavaScript, etc.)
DevOps: Experience with containerization, cloud services, and MLOps
Domain knowledge: Understanding of your specific use case and requirements

Many teams find success by upskilling existing team members rather than hiring specialized "agent engineers."

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