The Rise of AI Agents: From Foundation Models to Autonomous Workflows

By 2025, the AI landscape has shifted from a focus on ever larger models to the practical deployment of AI agents, autonomous systems that reason, plan, and execute tasks with minimal human oversight. At Sequoia Capital’s AI Ascent 2025, a central theme emerged: the future lies not in more powerful models but in more capable agents that drive transformative outcomes across industries. This blog explores the rise of AI agents, the emerging Agent Economy, and the mindset shifts required to harness their potential, offering a vision of how these systems are redefining work.

This transition marks a profound platform shift. AI agents are no longer mere tools; they are autonomous collaborators reshaping workflows and unlocking unprecedented leverage. From automating complex processes to collaborating in dynamic ecosystems, agents are poised to redefine how digital and physical work is orchestrated at scale.

From Tools to Teammates

Over the past decade, AI has evolved from static predictive tools, such as recommendation algorithms or basic chatbots, to dynamic copilots that assist with tasks like code completion or content drafting. Now, a new leap is underway: AI agents that not only suggest actions but execute them autonomously. These agents connect to APIs, manage documents, route tasks, and optimize decisions across interconnected systems, effectively acting as teammates rather than tools.

What distinguishes these agents? They exhibit four key characteristics:

• Goal driven: Unlike traditional software that follows rigid instructions, agents pursue objectives with adaptability. For example, an agent tasked with optimizing a supply chain can analyze data, adjust inventory, and coordinate with suppliers, responding dynamically to disruptions.
• Persistent: Agents retain memory across interactions, enabling long term context and refinement. A customer service agent, for instance, can learn from past interactions to personalize responses over time.
• Composable: Agents interoperate with other agents, tools, and data sources, creating modular workflows. A marketing agent might collaborate with a content creation agent and an analytics agent to execute a campaign end to end.
• Secure and accountable: With autonomy comes the need for robust governance. Identity management, audit trails, and safety boundaries ensure agents operate within defined parameters, maintaining trust and reliability.

This evolution is already visible in enterprise applications. Agents are automating workflows like contract analysis, IT incident response, and logistics optimization, reducing human effort and enabling seamless integration with existing systems.

The Agent Economy Is Emerging

At AI Ascent 2025, Sequoia partner Konstantine Buhler introduced the concept of the Agent Economy, a future where swarms of AI agents collaborate, negotiate, and transact value, much like microservices but with autonomy and learning capabilities. This is not just about individual agents; it is about a networked ecosystem where agents transfer resources, track trust and reliability, and operate alongside humans in a dynamic economy.

The Agent Economy hinges on solving significant technical challenges:

• Persistent Identity and Memory: Agents need consistent personalities and the ability to remember users over time. True memory and self learning remain complex hurdles, requiring advancements in contextual storage and retrieval, such as vector databases or knowledge graphs.
• Seamless Communication Protocols: Just as TCP/IP enabled networked computing, standardized protocols are essential for agents to exchange information, value, and trust. Emerging frameworks, like hypothetical protocols inspired by multi agent systems, aim to facilitate agent to agent collaboration, but broader adoption is needed.
• Security and Trust: When agents operate autonomously, often without direct human interaction, ensuring security and trust becomes critical. This is spawning an industry focused on agent authentication, encryption, and explainability, ensuring alignment with human intent.

This vision is taking shape. In logistics, agents optimize shipping routes and negotiate with carriers. In software development, agents autonomously manage code reviews and bug fixes. The Agent Economy promises a future where agents form complex networks, akin to neural networks, collaborating to solve problems at scale.

Vertical Agents Will Win

While general purpose assistants offer broad utility, the greatest impact will come from vertical agents, specialized systems tailored to specific industries and workflows. These agents embed deep domain expertise, integrate seamlessly with existing processes, and deliver targeted outcomes. For example:

• Legal Operations: Agents that parse contracts, flag risks, and automate compliance checks streamline legal workflows.
• Finance: Agents that monitor markets, execute trades, and manage portfolios enhance decision making efficiency.
• Healthcare: Agents that triage patient data, recommend treatments, and coordinate care accelerate diagnostic and treatment processes.

At AI Ascent, Sequoia emphasized a shift from models to outcomes. General purpose models excel at flexibility but often lack the precision required for complex workflows. Vertical agents, by contrast, leverage industry specific knowledge and integrations to deliver measurable value, transforming how businesses operate.

Mindset Shifts for the Agent Economy

The rise of AI agents and the Agent Economy demands new ways of thinking and managing. To fully embrace this paradigm, leaders and builders must adopt three key mindset shifts:

• The Stochastic Mindset: Traditional computing is deterministic, but AI agents operate probabilistically, introducing variability in outcomes. Embracing this uncertainty, understanding that agents may not always produce identical results, requires a shift from rigid expectations to adaptive strategies.
• A Management Mindset: Managing agents is akin to managing human teams. Leaders must define what agents can and cannot do, set clear boundaries, and monitor performance, balancing autonomy with oversight.
• Leverage with Less Certainty: Agents offer unprecedented leverage, enabling individuals and organizations to achieve more with fewer resources. However, this comes with increased uncertainty and risk. Mastering this trade off, leveraging agents’ capabilities while mitigating risks, is critical.

These shifts are already reshaping businesses. Companies are scaling faster with fewer people, moving toward a future where a single individual could, in theory, achieve the impact of a unicorn startup. While the one person unicorn vision has not yet fully materialized, the trajectory points to complex networks of agents driving exponential productivity.

What This Means for Builders

For founders, developers, and enterprise leaders, the rise of AI agents demands a strategic pivot:

• Build Workflows, Not Features: Focus on end to end solutions that integrate agents into existing processes. Instead of standalone tools, develop agents that automate entire workflows, such as customer onboarding from data collection to CRM updates.
• Prioritize Outcomes Over Demos: Impressive prototypes are insufficient; agents must deliver tangible business value, such as cost savings or productivity gains. Builders should focus on real world impact over technical novelty.
• Design for Trust and Extensibility: Agents must be secure, auditable, and interoperable. Incorporating explainability builds user trust, while composability enables agents to integrate into broader ecosystems, fostering scalability.

There is a shift from monolithic applications to agent driven ecosystems, where specialized agents collaborate to solve complex challenges.

Final Thoughts

AI agents represent a new abstraction layer in technology, a leap from static tools to autonomous collaborators capable of executing meaningful work. They are not just smarter bots, they are the backbone of intelligent workflows, poised to transform industries from finance to healthcare. The Agent Economy, with its networks of collaborating agents, promises unprecedented leverage, enabling organizations to scale faster and achieve more with less.

As the infrastructure matures, identity frameworks, communication protocols, and security systems, use cases will proliferate. The winners will be those who embrace the stochastic and management mindsets, build with agents at the core, and deliver specialized, outcome driven solutions. The shift is underway, and the future belongs to those who can harness this new paradigm to redefine how work gets done.