Agentic AI Automation

Agentic AI generally refers to AI systems that possess the capacity to make autonomous decisions and take actions to achieve specific goals with limited or no direct human intervention.

Key aspects of agentic AI

Autonomy

Agentic AI systems can operate independently, making decisions based on their programming, learning, and environmental inputs.

Goal-oriented behaviour

These AI agents are designed to pursue specific objectives, optimising their actions to achieve the desired outcomes.

Actions

An agentic AI interacts with its surroundings, perceiving changes and adapting its strategies accordingly.

Learning capability

Many agentic AI systems employ machine learning or reinforcement learning techniques to improve their performance over time.

Workflow optimisation

Agentic AI agents enhance workflows and business processes by integrating language understanding with reasoning, planning, and decision-making.

Multi-agent and system conversation

Agentic AI facilitates communication between different agents to construct complex workflows. It can also integrate with other systems or tools.

Why organizations should pay attention

In the fast lane of technological evolution, BDB is an Early Adaptor of Agentic AI

Agentic AI offers significant advantages in efficiency, decision-making, and customer interaction. By automating routine tasks and providing intelligent insights, agentic AI can help organizations save time, reduce cost, and improve overall productivity. Moreover, organizations who adopt an agentic AI system can gain a competitive advantage by leveraging its capabilities to innovate and enhance their business operations. BDB ensures Lower cost to entry and economies of scale
Agentic AI systems are designed to adapt to changing business environments, driving higher productivity and enabling organizations to stay competitive. For example, agentic AI can predict market trends and customer preferences, allowing businesses to tailor their strategies proactively. This adaptability not only improves efficiency but also fosters innovation, giving companies a significant edge over competitors.
Moreover, agentic AI systems can handle large volumes of data and extract actionable insights, which can be used to optimize operations and enhance customer experiences. By automating routine tasks, these systems free up human resources to focus on more strategic initiatives, thereby increasing overall organizational agility and responsiveness.

Enhanced decision-making

Agentic AI systems can analyse vast amounts of data quickly and accurately, providing valuable insights to inform better decision-making. Businesses can leverage these insights to optimise revenue and operations, identify market trends, and make data-driven decisions.
Boosted efficiency and productivity

Agentic AI can significantly enhance business efficiency and productivity by automating routine tasks and processes. In manufacturing, AI-driven robots can manage repetitive tasks with precision and consistency, reducing errors and increasing output.
Improved customer experience

By integrating agentic AI, businesses can offer personalised and responsive customer experiences.

BDB AI Agent Foundation Layers

01

Data Layer

The foundation integrates and unifies data from diverse sources, such as databases, and cloud storage; this layer provides flexibility and Role Based Access

02

Semantic Layer

(Data Catalog)
The semantic layer maps relationships between data points, ensuring context-aware insights by unifying data meaning across diverse sources, this layer translates complex data into business-friendly terms

03

Agentic Layer

The conversational engine that powers interactive, human-like discussions with data. AI agents understand your queries, interpret intent, and respond contextually, allowing seamless refinement and follow-ups.

04

Action Layer

The interface where insights are delivered in intuitive formats like natural language responses, dynamic visualizations, and real-time dashboards—making data actionable for everyone. This layer helps in triggering alerts, jobs, or any other user-defined actions.

BDB AGENTIC LAYER

Prebuilt Agents
These are ready-to-use agents designed to assist with specific tasks, enabling quick deployment and productivity.

Data Assist: Simplifies data analysis tasks, such as comparative analysis, normalization, and visualization.
Document Assist: Supports efficient document processing, summarization, and retrieval.
Code Assist: Enhances developer efficiency by generating code, test cases, annotations & resolve errors.

Build your own Agents

Leverage the low-code interface to create agents tailored to your needs:

Steps to Build Custom Agent Flows : Integrate Triggers like API Ingestion , Email listener etc Define Agent:

Configure LLM
Agent Role & Description
Task Definition & its Expected Output

Integrate Tools & Actions:

Connect with prebuilt tools
Define your own custom action
Connect to another Agentic flow

Pre-Built Agents : Data Assist

BDB Data Agent

1&5

CHAT BASED USER INTERFACE

Captures User Input
Shows Response to User

2

BDB DATA AGENT

Acts as the central controller, managing data processing and interactions
Identity User
Configures the Data Access

3

ORCHESTRATION ENGINE

Coordinates different agents and their functionalities.
Logging & Monitoring – Tracks system performance.
Memory Management – Manages system resource allocation.

3A

INTENT CLASSIFICATION AGENT

Rulebook/SOP – Utilizes predefined rules for intent recognition.
Rule-Based Parsing – Classifies intent and creates an execution plan.

3B

RETRIEVAL AGENT (CONTEXT ENGINE)

Data Catalog – Manages metadata and available datasets.
Data Extraction & Indexing – Ingests and indexes data.
Vector DB – Supports Qdrant, Clickhouse, Pinecone, etc.

3C

MODEL ENGINE

Supports models from OpenAI, Gemini, Amazon, Anthropic
Meta(Llama), Mistral AI, DeepSeek

3D

SEARCH AGENT

Role-Based Access Control for Dashboards, KPIs/Metrics, Reports, Data Store
Tables Metadata
Files Uploaded by user

3E

DATA AGENT

SQL Agent – Executes queries.
Query Execution – Retrieves and processes data.
Result Analysis & Insights – Extracts meaningful information.
Output Guardrails – Ensures data reliability.

3F

VISUALIZATION AGENT

Data Retrieval – Fetches data from the Data Agent.
Visualization Suggestion – Recommends visual formats.
BDB Widget Library – Provides predefined visualization widgets.

4

ACTION (TOOL) ENGINE

Trigger Alerts & Emails
Run Machine Learning Models
Trigger Workflows

BDB AI Agents : KEY Components

MODEL ENGINE

At the most basic level, agents must be able to reason over unstructured data.
BDB provides integration with proprietary model APIs like OpenAI, Anthropic, Gemini & more.
Self-hosted open-source models like Llama, Qwen2.5, Deepseek etc., which are self-hosted for data security.

CONTEXT ENGINE : KNOWLEDGE BASE & MEMORY MANAGEMENT

In addition to general knowledge, agents require external memory to store and recall domain-specific knowledge and the bounded context of the problem they are being tasked with solving, often via a vector database like Qdrant/ClickHouse.

ACTION (TOOL CALLING) ENGINE

Agents use tools to perform tasks that enhance their problem-solving capabilities. BDB provides prebuilt tools & ability to define custom tools for specific tasks and actions.

ORCHESTRATION (PLANNING) ENGINE

Acts as the central coordinator for all other components.
Identifies the intent from user input and orchestrates the generation of appropriate responses.
Manages the flow of information between Agents.
Handles error scenarios and fallback mechanisms.

Build your own agents:

For Automation of Your Existing Workflows

BDB Agentic Workflows

Vision for Agentic Automation

AI Agents are programs where LLM outputs control the workflow

Key Additions:

Agent components (e.g., agent engine, decision-making modules)
AI/ML Model Integration points
Custom Tools Development & Integration with Agents
Feedback loops for learning and improvement

Emphasize key principles:

Modularity and flexibility
Scalability and performance
Security and reliability

Benefits of Agentic Automation

Maximized Workflow Efficiency - Optimizes data processing paths, handles unstructured data efficiently, and increases productivity through intelligent workflow automation
Proactive Problem Solving - Automatically detects and resolves issues before they impact operations, minimizing system downtime and reducing human intervention
Autonomous Decisions - Improves accuracy in data-driven decisions through advanced analytics and pattern recognition, while reducing human error
Better Customer Experience - Delivers faster processing times, better data quality, and more reliable service through automated quality controls and efficient handling

AGENTIC AI Use CASES

INVOICE PROCESSING AGENTS

Document Intake Agent

Monitors file storage for new invoices
Validates file formats and quality
Prioritizes processing based on business rules

Extraction Agent

Performs OCR and text extraction
Handles multiple document layouts
Self-corrects recognition errors using context
Maps fields to standardized schema

Processing Agent

Extracts entities (amounts, dates, line items)
Validates against business rules
Updates databases
Generates embeddings for semantic search
Detects anomalies (unusual amounts, duplicate invoices)

DATA QUALITY AGENTIC PIPELINE

Profiling Agent

Analyses data distributions
Identifies patterns and relationships
Generates data quality metrics

Data Cleansing Agent

Fixes common data issues
Standardizes formats
Handles missing values
Resolves duplicates

Validation Agent

Enforces business rules
Checks referential integrity
Validates transformations
Reports quality metrics

VEHICLE IoT MONITORING & MAINTENANCE PIPELINE

Vehicle Telemetry Agent

Processes real-time sensor data
Monitors engine performance
Tracks fuel consumption
Analyses driver behaviour

Predictive Maintenance Agent

Forecasts maintenance needs
Schedules preventive service (via APIs)
Tracks parts lifecycle
Optimizes repair timing
Manages service history

Why BDB is optimally positioned to lead data-centric agents

Best Practices Implementation

BDB has accumulated a wealth of knowledge from implementing 1000+ diverse data pipelines and workflows across 10+ different industries. This experience has allowed BDB to identify patterns, best practices, and common challenges in data processing and automation. BDB has developed multiple solutions across domains like Telecom, Automobile , Industry 4.0 , Retail, Education. These insights are getting embedded into Agentic AI workflows, ensuring that BDB leads the early Adoption of Data Centric Agents.

Leverage BDBs training & solution ecosystem

BDB starts small using 100's of prebuilt connectors, research the merits of Agentic AI and Implement in a logical manner where Customer appreciates the outcome of Agents

Customer Centric Approach

BDB has experimented with multiple datasets and created training content in 10+ verticals. This helps not only internal BDB team but Partners and Customer data engineers to learn when and how to go for Agentic AI workflows

BDB is Flexible and Cost Effective

Implement Robust Security and Privacy using inbuilt features in BDB, Utilize the Data Catalog and Data Quality Modules to push quality data to Agentic AI to get the maximum value

BDB is Modern E2E, Scalable AI Platform

It is important to calculate all kinds of costs i.e. Infrastructure, Training, Implementation and Maintenance Costs. BDB helps customers try small, take customer feedback and then implement to ensure success. BDB charges based on user adoption and usage

Long term Sustainability

BDB helps customers develop a long-term AI Strategy which is future proof, as BDB is R&D focused and keeps developing newer features with technological advancements. With its expert consultancy, focus on customer success and complete support, BDB ensures that customers AI Investments continue to deliver value over time, in an ethical manner.