The Emerging Agent Economy
The next competitive edge is built—not hired
In 2024, HubSpot’s CTO Dharmesh Shah introduced Agent.ai—a concept for a professional network built not for people, but for AI agents.
Shah's vision is grounded in a simple, disruptive idea: “There’s an agent for that.”
Not just a software tool—but a task-performing, decision-making, and network-aware agent capable of taking on roles inside workflows and organizations.
The shift underway is not just technological—it is structural. We are entering a phase where AI agents begin to operate as economic actors, creating the conditions for an agent-based economy. This post outlines the four key features defining that shift.
1. Agents as Economic Actors
AI agents are evolving from tools into participants within workflows. These agents can:
Offer specialized services (e.g., research, design, policy analysis)
Discover and collaborate with other agents
Negotiate and execute tasks across platforms and organizations
Agents will represent individuals, companies, or institutions, operating in environments that require both routine and strategic action. They will not just assist knowledge workers—they may replace specific contracting roles altogether.
You may not hire a freelance researcher in the future. You may select a vetted research agent, complete with performance metrics and domain-specific training.
2. Discovery and Reputation Layers
A functioning agent economy requires infrastructure for search, trust, and transparency—just as Web 2.0 required search engines, recommendation engines, and user reviews.
Core features will include:
Registries for public and private agent access
Trust frameworks that validate credentials and training sources
Performance tracking and audit logs
Talent indexing based on proven capabilities (e.g., “policy synthesis agent with 93% accuracy across 12,000 cases”)
The idea of a "LinkedIn for agents" is not an abstraction. It reflects the emerging need for machine-readable reputation signals—a system where agents are evaluated not just on static specs, but on dynamic, verified outcomes.
3. Composable Agent Systems
The future will not be shaped by singular super-agents. It will be built through composable agent teams.
Agents will be modular—each responsible for a distinct function. Organizations will compose systems from specialized agents such as:
Personal assistant agents
Market or geopolitical analysts
Designers or brand advisors
Risk modellers or strategic planners
These agents will be swappable, upgradable, and licensed individually or in bundles. Workflows will be increasingly managed by orchestration layers that coordinate multiple agents in real time.
An organization might not develop its own internal tooling—but instead subscribe to a high-performing agent team designed for a specific vertical or operating environment.
4. Market Differentiation Through Agent Design
Organizations will no longer compete only on talent and human expertise. Strategic advantage will come from:
The quality and precision of their proprietary agents
The custom workflows built around those agents
The private data and context used to train them
In this economy, owning a high-performing agent is equivalent to holding intellectual property. Agents become institutional memory, product, and advantage.
This represents a fundamental shift: value is no longer just in human insight, but in agent architecture and orchestration.
Strategic Implications
This transition alters how organizations build capacity. It introduces new questions:
Who owns agent-generated insights?
How are agents credentialed or trusted across sectors?
What does compliance look like in a multi-agent system?
How do agent portfolios compete in open markets?
These are not long-term hypotheticals—they are current design challenges in AI ecosystems.
Where to Start
While early examples include Agent.ai, the architecture of the agent economy is being developed across open-source platforms (LangGraph, CrewAI), enterprise tools (Microsoft Copilot Studio), and experimental frameworks (Meta’s OpenAgents, Google’s Project Astra).
Understanding how agents will be discovered, selected, composed, and trusted is central to navigating the next phase of digital transformation.
The LinkedIn layer may only be one part of the equation—but it signals something important: In the Agent Economy, even bots need a network.
