From a Software Economy to an Agent Economy

We’re living through one of the most profound shifts in the Software economy yet. The rise of AI agents isn’t just another wave of automation, it’s a fundamental reimagining of who and what uses software, how it’s delivered, how it’s built. At Moonsong Labs, we call this the Agent Economy.

Similar to how the Internet connected people to information and services, the Agent Economy links software, data, and payments into intelligent networks of agents. This isn’t a distant future, it’s happening now, and its consequences are already tangible and far-reaching. This post aims to simplify some of the big moving pieces, showcase the opportunity, and share some of our foundational theses along the way.

The 3 Battlegrounds of the Agent Economy

1. A New Persona: Agents as First-Class Consumers

It’s time to think of agents as a first-class personae for your software and services. Today’s agents are already more sophisticated consumers:

• Agents read your documentation by crawling your site, consult context-efficient intermediary artefacts like llms.txt, or embed your documentation directly (see Cursor) to assist developers working with your API or library. The latest agentic development trend— called “vibe coding”—is becoming so prevalent that, in many cases, the agent can be the sole touchpoint between what a user wants and your service, library, or documentation.
• Agents integrate directly with your APIs, either by calling them directly or by using intermediate tools or client/server standards such as MCP. LLMs can can make decisions autonomously based on context and a variety of new signals to determine when to use your service as part of an agentic workflow, introducing new dynamics for discovery and growth.
• Finally, Agents can interact with your web application interfaces extracting content (see Firecrawl), referring traffic (see Exa), or directly automating user tasks (see Manus).

But the shift goes way deeper: intuitively, agents themselves can benefit directly from the adaptive interfaces of other agents. We are starting to see the emergence of interoperability standards that allow multi-agent systems to span organizational boundaries.

In short, this means your product must now serve both humans and AI agents, through flexible interfaces designed for each. Ignore this new persona at your peril.

2. New Interfaces: Adaptive, Generative, and Toolified

Interfaces themselves are morphing as well. Static UIs and fixed APIs are giving way to dynamic, generative interface, where LLMs not only fill in content but actively select and assemble UI components or parameterize API calls on the fly. This agentic layer enables interfaces to adapt in real time to the emergent needs and contexts of users and agents alike.

Meanwhile, service APIs are evolving into tools, accelerated by standards like MCP, and are becoming directly discoverable and executable by agents, not just humans. (see Smithery)

As briefly discussed earlier, a new frontier of interaction is also emerging: software vendors are beginning to expose their functionality as agent services, allowing the creation of multi-agent systems on the fly across organisation boundaries (see Google A2A announcement).

These new interfaces can serve as adaptive, “just-in-time” intelligent fronts for your product, abstracting away complexity, streamlining service discovery, and even enabling new innovative pricing or accountability models.

It may not be obvious today, but the flexibility of these novel agentic interfaces creates a powerful new channel for capturing both user and developer needs. Unstructured, conversational input and just-in-time workflow provides richer, real-time feedback on intent and experience, enabling software developers to adapt and improve their products more rapidly than ever before. (See Magic Input Box)

3. Building Software: Cheaper, Agentic, and Just-in-Time

Perhaps the most profound long-term change is at the very core of software life cycle. We’ve already seen how agents empower developers with increased productivity. A second-order effect comes in the form of a significant reduction in the cost of producing and maintaining software, opening up new unit economics for companies to tackle their markets. We expanded on this in our previous post, The Rise of the Agent Economy.

But it’s not just about how software is built—it’s also about what kinds of software becomes possible. Not only can we do more for less, but we can also address a broader array of use cases by leveraging agents to deliver backend services. While we’re only seeing the early stages of this trend, we believe it’s the beginning of something much bigger.

Imagine agents orchestrating the just-in-time assembly and integration of backend services, or even generating the code required to fulfill a user’s request on the fly. This new paradigm slashes the cost of personalization / customization and opens up entirely new avenues for innovation.

A key enabler of this shift beyond the rising performance of models is the concept of shared memory and collective learning among agents. When one agent solves a problem or builds a new capability, that knowledge and experience can be instantly leveraged and discovered by other agents, accelerating improvement and compounding innovation across the network.

This unlocks new economies of scale and context, making it possible for software providers to deliver highly adaptive, domain-specific solutions (think vertical SaaS or even operating-systems-as-a-service) that capture and serve the full unique context of a user’s needs.

As the cost of software delivery continues to decline, the competitive landscape will increasingly reward those who can leverage the agent development cycle to rapidly test, learn, and adapt to users’ needs—transforming software from a static product into a living, evolving organism.

Conclusion

The Agent Economy is transforming the fundamental landscape of SaaS. We’re witnessing a profound shift defined by three core pillars: (1) emergence of agents as first-class consumers; (2) the evolution of interfaces designed to seamlessly serve both humans and agents ; and (3) groundbreaking innovation in how software is built and delivered- all of which usher in adaptive, just-in-time solutions powered by Generative AI and agent-driven workflows.

This evolution presents an extraordinary opportunity, promising entirely new markets and rewarding those who move decisively. For both startups and established enterprises, the chance to pioneer and shape these new competitive arenas is unprecedented.

At Moonsong Labs, we’re currently dedicated to one of the most essential elements of this new paradigm: enabling agents to learn collaboratively, share insights, and evolve collectively. Our work unlocks transformative possibilities, from adaptive systems capable of dynamically assembling themselves on-demand, to agents that swiftly learn and gain proficiencies on new unseen ecosystems by drawing upon the collective knowledge and experiences of other agents. (see Entourage Spark)

Our latest venture, Entourage, is purpose-built to empower this vision—a platform designed for networked code agents which can learn, adapt, and collaborate asynchronously. We’re actively seeking visionary builders, strategic partners, and passionate innovators to join us to lay the foundational infrastructure for the Agent Economy.

If this vision resonates with you and you’re eager to help shape the future of software, we’d love to connect. Let’s build this future together.