What is Agent Composition?

What Is Agent Composition?

Agent Composition is the practice of building powerful AI agents by combining smaller, specialized components rather than trying to create a single monolithic agent that does everything. Think of it like assembling a team of specialists rather than hiring one person who claims to be an expert in everything.

In a composed agent system, each component handles a specific capability — one might be responsible for understanding user intent, another for retrieving relevant data, a third for generating responses, and a fourth for executing actions. These components are connected together in a defined flow, where the output of one becomes the input for the next, creating an end-to-end system that is far more capable than any individual component.

Why Composition Matters

The alternative to composition is building monolithic agents — single, large systems that try to handle every aspect of a task internally. Monolithic agents create several problems that composition solves:

• Complexity becomes unmanageable — As you add more capabilities to a single agent, the system becomes increasingly difficult to understand, test, and maintain
• Failures cascade — If one part of a monolithic agent breaks, the entire system can fail. In a composed system, individual components can fail gracefully without taking down the whole pipeline
• Reusability is lost — A capability built into a monolithic agent cannot easily be reused in other contexts. Composed components can be shared across multiple agent systems
• Iteration is slow — Improving a monolithic agent requires testing the entire system. Composed components can be upgraded individually

How Agent Composition Works

Component Design

Each component in a composed agent system has a clearly defined purpose, input format, and output format. For example:

• Intent classifier — Takes user input, outputs a categorized intent (e.g., "order inquiry," "complaint," "product question")
• Data retriever — Takes an intent and context, outputs relevant information from databases or knowledge bases
• Response generator — Takes retrieved information and context, outputs a natural language response
• Action executor — Takes a structured action request, executes it against backend systems, and returns the result

Composition Patterns

There are several common patterns for composing agent components:

Sequential pipeline — Components execute one after another in a fixed order. User input flows through intent classification, then data retrieval, then response generation. This is the simplest pattern and works well for straightforward workflows.

Branching pipeline — After an initial classification step, the flow branches to different component chains depending on the type of request. A product question might flow through a knowledge base retriever, while an order issue might flow through an order management component.

Parallel execution — Multiple components run simultaneously on different aspects of the same request. For example, a sentiment analyzer and an intent classifier can process the user's message at the same time, with their combined outputs feeding into the response generator.

Iterative loops — Components can call each other in loops, refining their outputs until a quality threshold is met. A response generator might produce a draft, pass it to a quality checker, receive feedback, and generate an improved version.

Business Benefits of Composition

Faster Development

Instead of building every capability from scratch, teams can compose agents from pre-built components — whether developed internally, sourced from open-source libraries, or purchased from AI vendors. This dramatically accelerates time to deployment.

Easier Maintenance

When a component needs to be updated — for example, upgrading the language model powering your response generator — you can swap it out without rebuilding the entire system. This modular approach reduces maintenance costs and risk.

Scalable Architecture

Different components can be scaled independently based on demand. If your intent classification component handles traffic easily but your data retrieval component is a bottleneck, you can scale just the retrieval component without over-provisioning the rest of the system.

Quality Control

Composition allows you to insert quality checks, filters, and guardrails between components. You can verify the output of each stage before passing it to the next, catching errors early rather than delivering flawed results to the user.

Composition in Southeast Asian Business Context

For businesses in ASEAN markets, agent composition offers practical advantages:

Multilingual Architecture

You can compose agents with language-specific components. A language detection component routes the input to the appropriate language processor, which then feeds into shared business logic components. This is more efficient and maintainable than building separate monolithic agents for each language.

Market-Specific Customization

Composed agents allow you to swap out market-specific components while keeping the core logic the same. A pricing component for Indonesia can be different from one for Singapore, while the product recommendation and customer interaction components remain shared.

Regulatory Compliance

Compliance checking components can be inserted into the composition pipeline to ensure that agent responses meet regulatory requirements in each market. This is especially important for financial services, healthcare, and other regulated industries operating across ASEAN jurisdictions with different rules.

Agent Composition vs. Multi-Agent Systems

While related, these concepts are different:

• Agent composition focuses on building a single agent from modular components. The components are tightly integrated and form one coherent agent experience
• Multi-agent systems involve multiple independent agents that collaborate or coordinate. Each agent is a separate entity with its own goals and capabilities

In practice, many enterprise AI systems use both: individual agents are built through composition, and those composed agents then participate in multi-agent systems for more complex workflows.

Key Takeaways for Decision-Makers

• Composition is the preferred architectural approach for building production-grade AI agents
• Start by identifying the distinct capabilities your agent needs and design each as a separate component
• Leverage pre-built components from AI platforms and vendors to accelerate development
• Plan for component reuse across multiple agent applications to maximize your investment
• Ensure clear interfaces between components so they can be upgraded or replaced independently