Skip to content

Agency vs Agents

Multi-agent systems are built from autonomous entities known as agents. These agents can perceive their environment, reason about goals, and perform actions. However, when many agents must cooperate to solve complex problems, the system requires more than individual intelligence. It requires structure.

This is where agency enters the picture.

Understanding the distinction between agents and agency is essential for designing scalable multi-agent systems. While agents perform the operational work of the system, agency defines the organizational framework that allows those agents to collaborate effectively.

In simple terms:

  • Agents act
  • Agency organizes

The relationship between these two concepts forms the foundation of AgencyGrid.

What is an Agent?

An agent is an autonomous computational entity capable of making decisions and performing actions in pursuit of goals.

Agents typically have several core capabilities:

  • reasoning about tasks and objectives
  • interacting with tools or external systems
  • communicating with other agents
  • adapting to changing environments

Agents operate based on their internal state, which may include knowledge, beliefs, preferences, and planning strategies.

Each agent is designed independently and may have its own internal architecture, reasoning methods, and capabilities.

For example, in a distributed problem-solving system, agents might include:

  • research agents that retrieve information
  • analysis agents that interpret data
  • execution agents that perform operations
  • reporting agents that generate outputs

Each agent contributes a specific capability to the overall system.

However, even when many capable agents exist, coordination is not guaranteed.

The Limits of Agent-Centric Coordination

In early multi-agent systems, coordination was often implemented directly within agents.

Each agent was programmed with rules describing how to interact with other agents and how to participate in collaborative tasks. The logic governing cooperation existed inside the agents themselves.

This approach works when:

  • the number of agents is small
  • the system is tightly controlled
  • all participating agents are designed together

However, as systems grow larger and more open, this approach becomes difficult to maintain.

Several problems emerge:

Embedded coordination logic

If coordination rules are embedded in each agent, modifying those rules requires updating every participating agent.

Limited interoperability

Agents built by different developers may not share the same interaction protocols or collaboration strategies.

Difficulty onboarding new agents

New agents must already understand the coordination logic of the system before they can participate.

Lack of visibility

Because the organizational structure exists only within agents, it cannot be easily inspected or managed.

These limitations make purely agent-centric systems difficult to scale.

What is Agency?

Agency is the explicit organizational framework that coordinates the actions of multiple agents.

Instead of embedding coordination logic within agents, the agency defines the structure in which collaboration occurs.

An agency specifies:

  • roles that participants can adopt
  • rules governing interactions
  • communication structures
  • responsibilities and permissions
  • institutional norms and policies

Agents interact with the agency rather than coordinating solely with each other.

Through this structure, the agency organizes agents into a functioning system.

The agency itself does not perform actions in the environment. Instead, it relies on agents occupying roles within the organization to execute tasks.

This separation allows the system to maintain a clear distinction between individual behavior and organizational coordination.

Agents Perform Actions

Agents are responsible for performing the operational work of the system.

They:

  • execute tasks
  • call tools
  • analyze data
  • generate outputs
  • interact with other agents

Agents bring capabilities and intelligence to the system.

However, agents operate according to their own internal reasoning processes. Each agent may pursue its own goals or apply its own strategies when solving problems.

Without a coordinating framework, these independent behaviors may lead to inefficiencies or conflicts.

Agency Organizes Collaboration

Agency provides the framework that ensures individual actions contribute to collective outcomes.

The agency defines:

  • how tasks are distributed
  • which agents can perform which roles
  • how agents communicate
  • how responsibilities are enforced
  • how conflicts are resolved

By providing this structure, agency allows agents with different capabilities and goals to collaborate effectively.

The agency therefore acts as the organizational layer of the system.

It transforms a group of independent agents into a coordinated organization.

Separation of Concerns

A key principle in AgencyGrid is the separation between agents and agency.

Agents focus on decision-making and task execution.

Agency focuses on organization and governance.

This separation provides several advantages.

Flexibility

Because the agency structure is defined independently of agents, new agents can join the system without redesigning the entire coordination model.

Interoperability

Agents with different internal architectures can participate in the same agency as long as they follow the agency’s interaction rules.

Observability

The structure of collaboration becomes visible and manageable because it exists as an explicit representation.

Adaptability

Agency structures can evolve without requiring modifications to the agents themselves.

Role-Based Interaction

In AgencyGrid, agents typically interact with the agency through roles.

A role represents a position within the organization and defines:

  • objectives associated with the role
  • responsibilities the role must fulfill
  • permissions granted to the role
  • interactions with other roles

Agents join the agency by adopting roles.

From the agency’s perspective, it does not matter which specific agent fills a role, as long as the agent satisfies the role’s requirements.

From the agent’s perspective, adopting a role involves evaluating whether the role aligns with its goals, capabilities, and incentives.

This role-based interaction model allows the agency to remain stable even as agents join, leave, or change.

Example: Agents Without Agency

Consider a system where multiple agents attempt to solve a complex research problem.

Each agent independently searches for information and generates reports. Because there is no coordinating structure:

  • multiple agents may duplicate the same work
  • agents may produce incompatible outputs
  • no mechanism exists to combine results effectively

Even though the system contains capable agents, the lack of organizational structure prevents efficient collaboration.

Example: Agents Within an Agency

Now imagine the same system operating within an agency framework.

The agency defines roles such as:

  • researcher
  • analyst
  • verifier
  • summarizer

Agents adopt these roles and perform the associated responsibilities.

The agency defines the sequence of interactions between roles, ensuring that:

  1. research agents gather information
  2. analysis agents interpret the data
  3. verification agents validate results
  4. summarization agents generate final outputs

The agency organizes the workflow so that each agent contributes effectively to the collective outcome.

Agency Enables Collective Intelligence

When agents operate within an agency, their individual capabilities become part of a larger system of coordinated problem solving.

The agency defines how tasks are distributed and how results are integrated.

This coordination enables a form of collective intelligence, where the combined actions of multiple agents produce results that no individual agent could achieve alone.

AgencyGrid formalizes this concept by introducing agency as a core component of multi-agent systems.

Instead of relying solely on the intelligence of individual agents, AgencyGrid enables the creation of structured organizations of agents that can collaborate, adapt, and evolve over time.

Agents and Agency as Complementary Concepts

Agents and agency should not be viewed as competing ideas.

They are complementary components of a larger system.

Agents provide the intelligence and capabilities needed to perform tasks.

Agency provides the structure that organizes those capabilities into coherent collaborative systems.

Together, they enable the creation of large-scale agent ecosystems capable of solving complex problems across distributed environments.