What is Agentic AI? Intelligent Agents and Autonomous Workflows

Organizations are increasingly turning to artificial intelligence (AI) to automate tasks and workflows, improve efficiency, and drive innovation. In CXOTalk episode 853, host Michael Krigsman and Praveen Akkiraju, Managing Director at VC firm Insight Partners, discuss the promising field of agentic AI. They discuss the capabilities, applications, and implications of AI agents for businesses, focusing on the pivotal roles of data infrastructure, trust, and transparency in successful implementations.

Akkiraju demystifies the complexities of agentic AI, presenting these technologies in a way that is accessible to business and technology leaders. He addresses concerns about job displacement, highlighting how these technologies can augment human capabilities and unlock greater productivity. The conversation also outlines vital factors to consider when evaluating agentic AI solutions—such as reasoning abilities, data requirements, and ethical considerations—empowering leaders to navigate the enterprise's rapidly evolving landscape of AI agents confidently.

Episode Highlights

Embrace Agentic AI to Drive Productivity Gains

• Focus on automating repetitive, non-value-added back-office tasks with agentic AI to unlock significant productivity gains across various business functions.
• View agentic AI as an evolution of traditional automation, offering enhanced dynamism and adaptability compared to older technologies like RPA.

Harness the Power of Reasoning and Planning in Agentic AI

• Prioritize AI agents with robust planning and reasoning capabilities to handle complex tasks and automate decision-making processes more effectively.
• Evaluate and experiment with techniques like chain-of-thought reasoning and reinforcement learning to enhance the accuracy and reliability of agentic AI systems.

Prioritize Data Infrastructure and Integration for Agentic AI Success

• Invest in establishing a solid data infrastructure with accessible APIs to empower AI agents with the information they need to execute tasks effectively.
• Utilize techniques like fine-tuning, RAG, and embeddings to optimize models for specific domain-specific data and use cases within your organization.

Build Trust and Transparency into Agentic AI Systems

• Implement safeguards, such as reflection loops and comprehensive testing, to ensure the accuracy and reliability of agentic AI outputs and build trust in the technology.
• Incorporate transparency features like provenance tracking and explainability into agentic workflows, enabling users to understand how AI agents arrive at their conclusions.

Adopt a Strategic Approach to Agentic AI Adoption

• Evaluate existing enterprise application architectures and identify opportunities to integrate AI agents for greater efficiency and automation.
• Focus on building deterministic systems around non-deterministic LLMs by carefully managing prompts, implementing guardrails, and leveraging suitable techniques to ensure reliable and predictable outcomes.

Key Takeaways

Unlock Productivity with Agentic AI

Adopting agentic AI offers businesses a significant advantage, enabling them to automate repetitive back-office processes. This drives substantial productivity gains across various functions and allows organizations to redirect human resources towards higher-value work, thereby increasing efficiency and potentially reducing operational costs.

Prioritize Reasoning and Planning in Your AI Strategy

Reasoning and planning capabilities play a pivotal role in the effectiveness of agentic AI. Evaluating AI agents based on their ability to handle complex tasks autonomously through techniques like chain-of-thought reasoning is crucial. This approach enhances the automation of decision-making processes and improves the accuracy of AI-driven outcomes.

Build Trust and Transparency into Agentic AI Implementations

Building trust in agentic AI is critical for its widespread adoption and achieving positive results. Investing in solutions that prioritize transparency and demonstrate how decisions are made and how data is used is essential. Additionally, organizations should implement governance frameworks and ethical guidelines to ensure responsible AI usage and avoid potential biases or negative consequences.

Episode Participants

Praveen Akkiraju is a Managing Director at Insight Partners. He brings a product and operational lens to investing in Automation, Data platforms, DevOps, and Infrastructure software. His investments include companies such as BigPanda, Bardeen, Reco, Rudderstack, and Workato. Praveen spent the early part of his career in the trenches, building products and scaling engineering teams to build world-class platforms in highly competitive market segments. He holds BS and MS degrees in Electrical Engineering and is an alumnus of the Harvard Business School.

Michael Krigsman is a globally recognized analyst, strategic advisor, and industry commentator known for his deep digital transformation, innovation, and leadership expertise. He has presented at industry events worldwide and written extensively on the reasons for IT failures. His work has been referenced in the media over 1,000 times and in more than 50 books and journal articles; his commentary on technology trends and business strategy reaches a global audience.