Key Takeaways
- Agentic AI systems go beyond providing rote, predetermined outputs by autonomously executing intricate workflows and making dynamic decisions to achieve defined outcomes
- While use cases are still in early development, agentic AI systems are beginning to demonstrate their value in areas like procurement, sales support, and customer service
- Organizations of the future may operate with millions of agentic AI systems under human supervision
- We believe the next wave of AI startups will likely be those that successfully harness agentic AI to unlock new efficiencies, products, and business models
Artificial Intelligence (AI) is evolving beyond static models and passive assistants into something far more dynamic—agentic AI. These systems don’t just respond to queries; they autonomously plan, execute, and adapt to complex objectives. In short, AI is moving from thought to action.
What is Agentic AI?
Agentic AI refers to AI systems capable of independent decision making, long-term goal pursuit, and iterative problem solving. Unlike traditional AI, which is often reactive and constrained to specific tasks, agentic AI operates with a degree of self-direction, dynamically interacting with its environment, other models, and humans, to achieve defined outcomes.
While agentic AI systems leverage the creative capabilities of generative AI models such as ChatGPT, they differ in key ways. First, instead of merely generating content, they are designed to make decisions. Second, unlike models that require human prompts, agentic AI autonomously works toward specific goals, such as increasing sales or improving customer satisfaction. Finally, these systems execute intricate workflows, accessing databases and initiating processes independently.
Why Now?
Several converging trends have set the stage for agentic AI:
- Advances in Large Language Models: Improved reasoning, planning, and multistep execution enable greater autonomy.
- Memory and Context Windows: Enhanced memory architectures allow AI agents to retain long-term context, improving performance on evolving tasks.
- Reinforcement Learning and Fine Tuning: AI agents can self-improve through reinforcement learning and environment interactions.
- Tool Use and API Interactions: Agentic AI is integrating with external APIs, databases, and robotic systems, allowing it to act beyond data processing.
What are the Potential Use Cases?
While many agentic AI applications are in early development, potential use cases are emerging across industries and functions. A few examples include:
Customer Service: Traditional customer bots are limited to predefined responses, while agentic AI customer service agents can act independently, based on their understanding of customer intent and emotions. For example, an AI agent could anticipate a delayed delivery, notify the customer proactively, and offer a discount to improve satisfaction. Startups such as Sierra, Ema, and Decagon are developing agentic AI chatbots that transform customer interactions by providing empathetic, conversational, and personalized support.
Procurement: Many procurement AI solutions enhance purchasing workflows. AI assistants function as knowledgeable co-workers, guiding employees through complex purchasing decisions by reviewing company policies and requirements. Companies such as Zip (an Adams Street portfolio company) are advancing procurement AI from assistive tools to fully autonomous agents. While current systems focus on data analysis and guided automation, Zip’s agentic AI framework aims to independently analyze data, optimize procurement operations, and ensure compliance with minimal human input.
A recent Capgemini survey of 1,100 business executives found that 50% will implement AI agents this year. Within three years, this number is expected to rise to 82%
Sales Support: Sales teams often struggle with lead identification and nurturing due to administrative burdens. Agentic AI systems have the potential to alleviate these challenges by handling repetitive tasks, allowing sales professionals to focus on high-value activities.
Rox is reimagining how sales teams work by offering an agentic CRM. The AI-powered system not only acts as a system of record by storing customer data, but also helps businesses understand their customers on a deeper level by predicting their needs and proactively engaging with them to drive revenue growth.
Another company, 11x, has developed Alice, a digital sales development representative that autonomously identifies key decision makers and schedules meetings. Mike, its second product, automates inbound and outbound calls in 28 languages in a personalized, low-latency phone call.
What Might Agentic AI Look Like in the Workforce?
In a recent interview,1 Jensen Huang, CEO of NVIDIA, envisioned a future where every employee acts as a manager overseeing AI agents. Huang predicts that NVIDIA’s 30,000 workforce may be accompanied by millions of AI agents. He is not alone in envisioning an agentic enterprise. A recent Capgemini survey of 1,100 business executives found that 50% will implement AI agents this year, up from 10% currently employing them. Within three years, this number is expected to rise to 82%.2
Glean (an Adams Street portfolio company) is building towards this agentic future with its recent launch of Glean Agents, a platform that enables enterprises to build, deploy, and manage AI agents at scale securely and compliantly.
What are the Possible Challenges and Risks?
Despite their significant potential, agentic AI systems are still at a relatively early stage of development. As such, there are obstacles in fully leveraging their potential, including:
- Control and Alignment: Ensuring agentic AI operates safely and aligns with human intentions.
- Regulatory Uncertainty: Increasing AI autonomy will attract scrutiny from regulators, necessitating proactive governance strategies.
- Compute Constraints: Running autonomous AI agents requires significant computational power, posing cost barriers.
- Ethical Concerns: Issues such as deepfakes, misinformation, and bias must be addressed to ensure responsible AI deployment.
What Comes Next?
We think the next wave of AI startups will likely focus on harnessing agentic AI to unlock new efficiencies, products, and business models. Whether through enabling infrastructure, verticalized AI agents, or hybrid human-AI collaboration tools, the companies that navigate this transition will likely define the next decade of innovation.
1. Consumer Electronics Show 2025 in Las Vegas, Nevada.
2. Capgemini Harnessing the value of generative AI: 2nd edition.
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