BDB's 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

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.
Adapts to 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.
Handles large data volumes 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 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.
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, with flexibility and Role-Based Access.

02

Semantic Layer

(Data Catalog)
Ensures context-aware insights by mapping relationships between data points and translating complex data into business-friendly terms.

03

Agentic Layer

The conversational engine that powers interactive discussions with data. AI agents understand queries, interpret intent, and respond contextually.

04

Action Layer

Delivers insights via natural language, visualizations, and dashboards—triggering alerts, jobs, and actions based on user needs.

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, and resolving errors.

Build Your Own Agents

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

Steps to Build Custom Agent Flows:

1. Integrate Triggers: Use API Ingestion, Email listener, etc.

2. Define Agent:

Configure LLM
Agent Role & Description
Task Definition & Expected Output

3. Integrate Tools & Actions:

Connect with prebuilt tools
Define your own custom actions
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.
Identifies 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

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

3B - Retrieval Agent (Context Engine)

Manages metadata and available datasets.
Data Extraction & Indexing – Ingests and indexes data.
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.
Handles Tables Metadata.
Processes Files Uploaded by user.

4 - Action (Tool) Engine

Trigger Alerts & Emails.
Run Machine Learning Models.
Trigger Workflows.

BDB AI Agents: KEY Components

Model Engine

Agents must reason over unstructured data.
Supports proprietary models like OpenAI, Anthropic, Gemini.
Provides self-hosted open-source models for security.

Context Engine: Knowledge Base & Memory Management

Agents require external memory to store and recall domain-specific knowledge.
Uses vector databases like Qdrant/ClickHouse.

Action (Tool Calling) Engine

Agents use tools to perform tasks that enhance their problem-solving capabilities.
Provides prebuilt tools & allows custom tool creation.

Orchestration (Planning) Engine

Acts as the central coordinator for all components.
Identifies user intent & orchestrates responses.
Manages flow of information between Agents.
Handles error scenarios & 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.
Proactive Problem Solving: Detects and resolves issues before they impact operations, minimizing downtime.
Autonomous Decisions: Improves accuracy in data-driven decisions while reducing human error.
Better Customer Experience: Ensures faster processing times, better data quality, and reliable service.

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

Processing Agent

Extracts entities (amounts, dates, line items)
Validates against business rules
Updates databases and detects anomalies

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

Validation Agent

Enforces business rules
Checks referential integrity
Reports quality metrics

VEHICLE IoT MONITORING & MAINTENANCE PIPELINE

Vehicle Telemetry Agent

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

Predictive Maintenance Agent

Forecasts maintenance needs
Schedules preventive service
Manages service history

Why BDB is optimally positioned to lead data-centric agents

Best Practices Implementation

BDB has accumulated knowledge from 1000+ diverse data pipelines across 10+ industries, identifying patterns and best practices to lead the early adoption of Data-Centric Agents.

Leverage BDB’s Training & Solution Ecosystem

BDB provides 100s of prebuilt connectors and logical AI implementation strategies, ensuring customers appreciate Agentic AI outcomes.

Customer-Centric Approach

BDB has created training content across 10+ verticals, helping internal teams, partners, and customers adopt Agentic AI workflows effectively.

BDB is Flexible and Cost-Effective

Utilize BDB’s inbuilt security, data catalog, and quality modules to ensure high-quality data flows into Agentic AI for maximum value.

BDB is a Modern, Scalable AI Platform

BDB helps customers start small, take feedback, and scale based on adoption, ensuring a cost-effective, success-driven AI implementation.

Long-Term Sustainability

With its R&D focus and continuous technological advancements, BDB ensures AI investments remain future-proof, ethical, and valuable over time.