Agentic Applications

Agentic applications represent a significant leap in AI, moving beyond simple chatbots to systems that can autonomously reason, plan, and execute multi-step tasks to achieve a defined goal. They are designed to operate with minimal human intervention, continuously learning and adapting over time.

Core Principles of Agentic Applications:

1. Autonomy: The ability to make decisions and act independently to achieve a goal.
2. Goal-Oriented: Designed with a clear objective in mind, which guides their actions.
3. Perception: Ability to collect, organize, and interpret data from their environment (e.g., user input, external systems, databases).
4. Reasoning and Planning: Utilizing an LLM or other AI models to understand tasks, generate solutions, and determine the necessary steps to achieve a goal.
5. Action Execution: The capability to interact with external tools, APIs, or systems to perform tasks. Memory and Learning: Storing past interactions, experiences, and knowledge to improve performance and adapt to new situations over time.
6. Adaptability: Continuously improving processes based on data and feedback.
7. Multi-Agent Collaboration (Optional but Powerful): Multiple specialized agents working together to solve complex problems.

Key Components of an Agentic AI Architecture:

1. Goal and Task Management:
   • Defines high-level objectives.
   • Breaks down complex goals into manageable subtasks.
   • Tracks progress towards completion and adapts goals as circumstances change.
2. Perception and Input Processing:
   • Gathers information from various sources (user queries, system logs, structured data from APIs, sensor readings, etc.).
   • Utilizes AI technologies like Natural Language Processing (NLP) for text, computer vision for images, or data extraction for structured data to interpret inputs.
   • Cleans, processes, and structures raw data into a usable format.
3. Memory and Knowledge Management:
   • Short-term memory: Stores information relevant to the current interaction or session.
   • Long-term memory: A persistent knowledge base (often using vector databases) that stores facts, accumulated data, and past experiences for semantic search and retrieval.
4. Reasoning and Planning Engine (Often Powered by LLMs):
   • The “brain” of the agent.
   • Understands the user’s intent and the overall goal.
   • Generates a plan of action, including the sequence of steps and which tools to use.
   • May use techniques like Retrieval-Augmented Generation (RAG) to access proprietary data for more accurate and relevant outputs.
5. Action and Execution Module:
   • Enables the agent to interact with the environment and perform tasks.
   • Connects to external tools, APIs, and systems (e.g., searching the internet, executing code, sending emails, interacting with databases).
   • Monitors execution and adjusts if needed.
6. Integration and Orchestration Layer:
   • Connects all the components, handling communication, scheduling, and workflow control.
   • In multi-agent systems, it orchestrates collaboration between different agents.
   • Ensures robust error handling and recovery.
7. Monitoring, Feedback, and Governance:
   • Continuously monitors agent actions and outcomes.
   • Implements feedback loops for continuous learning and correction.
   • Enforces policies (security, ethical, performance standards) and provides audit trails.

graph TD
    %% User as initiator
    User((User))
    
    %% External Systems
    ExtSys[External Systems & Tools]
    
    %% Core Agentic Components
    subgraph "Agentic AI System"
        Perception[Input Processing]
        Memory[Memory & Knowledge]
        Brain[Reasoning Engine<br/>LLM]
        TaskMgmt[Goal Management]
        Action[Action Execution]
        Monitor[Monitoring & Feedback]
    end
    
    %% Main Data Flow - User initiates the process
    User -->|"Provides goals,<br/>queries, tasks"| Perception
    Perception --> Brain
    Brain --> TaskMgmt
    TaskMgmt --> Action
    Action --> ExtSys
    
    %% Memory Integration
    Brain <--> Memory
    
    %% Feedback Loop
    Action --> Monitor
    Monitor --> Brain
    
    %% Results back to user
    Monitor -->|"Results,<br/>status updates"| User

Steps to Build an Agentic Application:

1. Define Your AI Agent’s Purpose and Goals:
   • Clearly articulate the problem the agent will solve and its specific objectives.
   • Decide the type of agent (reactive, learning, hybrid, multi-agent).
2. Set Up Your Development Environment:
   • Install Python (if using Python, which is common for AI/ML).
   • Set up a virtual environment to manage dependencies.
   • Install essential libraries (e.g., numpy, pandas, langchain, crewAI, autogen, LLM APIs like OpenAI, Google Cloud, etc.).
3. Collect and Prepare Data:
   • Gather relevant, high-quality data for your agent’s task (e.g., customer behaviors, transaction histories, documents for RAG).
   • Clean, preprocess, and label the data as needed.
4. Choose and Train Your AI Model (if applicable):
   • Select appropriate machine learning algorithms or leverage pre-trained LLMs.
   • Fine-tune models if necessary with your specific data.
5. Develop Agent Tools:
   • Create functions or APIs that your agent can call to interact with external systems or perform specific actions (e.g., a search tool, a code interpreter, a database query tool, an email sending tool).
   • Provide clear natural language descriptions for each tool and its parameters so the LLM can understand how and when to use them.
6. Implement the Agent Logic (Orchestration):
   • Prompt Engineering: Craft effective prompts for your LLM that define the agent’s role, available tools, and how it should reason and plan.
   • Event Loop: Implement an event loop where the agent:
     • Receives input (perception).
     • Determines the next steps using the LLM (reasoning/planning).
     • Invokes appropriate tools (action execution).
     • Updates its internal state or memory.
     • Continues looping until the goal is achieved or instructed to stop.
   • Memory Integration: Integrate short-term and long-term memory components to maintain context and allow for learning.
7. Build a User Interface (UI):
   • Create an intuitive UI for users to interact with your agent (e.g., a chatbot interface using Streamlit, Gradio, or a web framework).
     • For example, PyWebIO the easiest possible way of building Python web app known by human civilization.
   • Test and Iterate:
     • Rigorously test your agent across various scenarios and inputs.
     • Monitor its performance, identify errors, and gather feedback.
     • Iterate on the agent’s logic, prompts, and tools to improve its effectiveness and reliability.
     • Pay attention to “hallucinations” and unexpected behaviors.
8. Deploy and Monitor:
   • Deploy your agentic application to a production environment.
   • Implement real-time monitoring to track performance, identify issues, and ensure scalability.
   • Establish a feedback loop for continuous improvement and updates.

Agent types

graph TD
    subgraph "Agent Types Classification"
        Reactive[**Reactive Agents**<br/>- Simple stimulus-response<br/>- Rule-based behavior<br/>- No learning capability<br/>- Fast execution]
        
        Learning[**Learning Agents**<br/>- Adaptive behavior<br/>- Experience-based improvement<br/>- Pattern recognition<br/>- Memory utilization]
        
        Deliberative[**Deliberative Agents**<br/>- Goal-oriented planning<br/>- Multi-step reasoning<br/>- State modeling<br/>- Strategic thinking]
        
        Hybrid[**Hybrid Agents**<br/>- Combines reactive + deliberative<br/>- Fast response + planning<br/>- Layered architecture<br/>- Context-aware switching]
        
        MultiAgent[**Multi-Agent Systems**<br/>- Distributed intelligence<br/>- Specialized roles<br/>- Collaborative problem-solving<br/>- Communication protocols]
        
        Cognitive[**Cognitive Agents**<br/>- Human-like reasoning<br/>- Knowledge representation<br/>- Complex decision-making<br/>- Self-awareness]
    end
    
    subgraph "Complexity Scale"
        Simple[**Simple Tasks**]
        Complex[**Complex Tasks**]
        
        Simple --> Reactive
        Simple --> Learning
        Complex --> Deliberative
        Complex --> Hybrid
        Complex --> MultiAgent
        Complex --> Cognitive
    end
    
    subgraph "Real-World Examples"
        Examples1[-- Chatbots<br/>\-- Recommendation Systems]
        Examples2[-- Autonomous Vehicles<br/>-- Trading Systems]
        Examples3[-- Research Assistants<br/>-- Strategic Planning Tools]
        
        Reactive -.-> Examples1
        Learning -.-> Examples1
        Deliberative -.-> Examples2
        Hybrid -.-> Examples2
        MultiAgent -.-> Examples3
        Cognitive -.-> Examples3
    end

Frameworks and Libraries:

Several frameworks and libraries simplify agentic application development:

1. LangChain: A popular framework for building applications with LLMs, providing tools for agent orchestration, memory, and tool integration.
2. CrewAI: A framework designed for orchestrating multiple AI agents to work collaboratively.
3. AutoGen (Microsoft): Enables building multi-agent conversations with customizable agents.
4. Spring AI: As a last resort. For Java developers, offers patterns like Chain Workflow and Parallelization Workflow for building LLM-based systems.
5. Amazon Bedrock’s AI Agent framework: A cloud-based solution for building and deploying agents.

By following these steps and leveraging the right tools, you can develop powerful agentic applications that automate complex workflows, enhance decision-making, and provide more intelligent and autonomous experiences.

Reference Architecture

Let’s examine a practical example of an agentic application designed to solve a real-world data analysis problem.

Problem Statement

Challenge: We have a database containing telemetry signals from physical devices with the following characteristics:

• Device metadata (name, location, status: healthy/unhealthy)
• Both current and historical data
• Complete history of device metadata and status changes
• Need for precise, accurate data extraction and insights

Why Traditional RAG Won’t Work:

• Requires precise answers, not approximate similarity-based responses
• Database queries need exact matching, not semantic similarity
• Critical operational data demands 100% accuracy

Proposed Agentic Architecture

Solution: A SQL-Agent that acts as an intelligent database interface with the following capabilities:

1. Domain Knowledge: Deep understanding of database schema and typical query patterns
2. Natural Language Processing: Converts user requests into precise SQL queries
3. Interactive Validation: Reviews generated queries with users before execution
4. Safe Execution: Executes approved queries and presents results in tabular format
5. Query Optimization: Suggests efficient query alternatives and explains results

Architecture Flow

graph TD
    User((User))
    
    subgraph "Telemetry Database"
        DB[(**Device Telemetry DB**<br/>- Device metadata<br/>- Status history<br/>- Location data)]
    end
    
    subgraph "SQL Agent System"
        NLP[Natural Language<br/>Processor]
        Schema[Schema Knowledge<br/>& Query Patterns]
        SQLGen[SQL Query<br/>Generator]
        Validator[Query Validator<br/>& Explainer]
        Executor[Safe Query<br/>Executor]
        Formatter[Results<br/>Formatter]
    end
    
    %% User interaction flow
    User -->|"Show me all unhealthy devices<br/>in Building A from last week"| NLP
    
    %% Query generation process
    NLP --> Schema
    Schema --> SQLGen
    SQLGen --> Validator
    
    %% User validation loop
    Validator -->|"Generated SQL:<br/>SELECT * FROM devices WHERE..."| User
    User -->|"Approve/Modify"| Validator
    
    %% Execution and results
    Validator --> Executor
    Executor --> DB
    DB --> Executor
    Executor --> Formatter
    Formatter -->|"Tabular Results<br/>+ Insights"| User
    
    %% Knowledge feedback
    Executor -.->|"Query patterns<br/>performance data"| Schema

Key Components:

1. Natural Language Processor: Interprets user requests and extracts intent
2. Schema Knowledge Base: Maintains understanding of database structure and common query patterns
3. SQL Query Generator: Converts natural language to precise SQL queries
4. Query Validator: Reviews queries for correctness and explains them to users
5. Safe Executor: Executes approved queries with proper error handling
6. Results Formatter: Presents data in clear, actionable tabular format

Example Interaction:

User Input: “Show me all devices that went from healthy to unhealthy in the last 24 hours”

Agent Response:

-- Generated Query for Review:
SELECT d.device_name, d.location, 
       prev.status as previous_status,
       curr.status as current_status,
       curr.timestamp as change_time
FROM devices d
JOIN device_history prev ON d.device_id = prev.device_id
JOIN device_history curr ON d.device_id = curr.device_id
WHERE prev.status = 'healthy' 
  AND curr.status = 'unhealthy'
  AND curr.timestamp > NOW() - INTERVAL 24 HOUR
  AND curr.timestamp > prev.timestamp
ORDER BY curr.timestamp DESC;

User: “Looks good, execute it”

Agent: Executes query and returns formatted results with insights about failure patterns

This architecture ensures precision while maintaining the autonomous, goal-oriented nature of agentic applications.

• Engineering
• Ai