Inside Agentic AI: Ravi Bommakanti on Transforming Enterprise Intelligence

Leading AI Innovation at App Orchid

Ravi Bommakanti, CTO at App Orchid, drives the company’s vision to embed AI deeply into enterprise applications and decision-making. Their flagship product, Easy Answers™, allows users to query data naturally, generating AI-powered dashboards, insights, and actionable recommendations.

What is Agentic AI?

Agentic AI marks a shift from static, rule-based AI to autonomous systems that can reason, plan, and collaborate dynamically. Unlike traditional AI which follows fixed paths, agentic AI decomposes complex tasks, identifies specialized agents through registries, and orchestrates their interaction, mimicking how expert human teams work.

Role of Google Agentspace and App Orchid

Google Agentspace accelerates agentic AI adoption by providing infrastructure for intelligent agents connected to work applications, leveraging Google's models like Gemini. App Orchid complements this by delivering semantic understanding through knowledge graphs that contextualize enterprise data, enabling agents to operate effectively.

Overcoming Adoption Challenges

Key hurdles include data quality, security for agent-to-agent trust, and managing distributed knowledge. App Orchid addresses these by building semantic layers, engaging business users to improve data reliability, implementing security frameworks for dynamic interactions, and enabling advanced orchestration through protocols like MCP and Agent2Agent.

How Easy Answers™ Works

Easy Answers connects securely to numerous data sources without replication, builds ontologies into Managed Semantic Objects (MSOs), enriches them with metadata, then interprets natural language queries with 99.8% text-to-SQL accuracy. It generates interactive visualizations ('curations') and offers Quick Insights for deeper ML-driven analysis.

Bridging Data Silos and Ensuring Trust

The platform creates a virtual semantic layer linking diverse data sources into a unified business language. It supports full traceability with data lineage, enabling users to audit every insight back to source data, and provides explainability through natural language summaries.

Transparency in Regulated Industries

Easy Answers ensures end-to-end data lineage, documents methodologies for ML models, and pairs insights with clear natural language explanations. Additional governance features include role-based access and audit logs to meet compliance requirements.

Turning Insights into Actions

Generative Actions propose concrete, context-aware recommendations for business outcomes, such as tailored retention offers. Human oversight remains central, with approvals before automated workflows trigger execution in operational systems.

The Power of Knowledge Graphs

Knowledge graphs and semantic models form the platform’s core, enabling natural language interaction, preserving rich business context, and allowing flexible adaptation as business needs evolve.

Supporting Foundational and Custom Models

App Orchid integrates leading AI models from Google, OpenAI, and open-source projects, while allowing organizations to bring custom AI/ML models via Python and cloud platforms like Vertex AI or SageMaker, linked through the semantic layer for business-user accessibility.

Future Trends in Enterprise AI

The next wave involves dynamic, composable agent ecosystems with marketplaces, standardized communication protocols, dynamic orchestration, and no-code agent design. App Orchid’s semantic foundation empowers these agents to collaborate effectively.

Evolving Role of the CTO

CTOs will transition to ecosystem architects, focusing on data strategy, new governance models for agentic AI, adaptability, and fostering human-AI collaboration, maximizing organizational intelligence rather than just managing infrastructure.

For more on App Orchid’s innovations, visit their website.