Why Build Your Own Agent Runtime

Modern agent frameworks provide powerful abstractions.

Examples include:

tool calling APIs
planning loops
memory management
workflow graphs

These tools allow developers to build complex systems quickly.

Example architecture:

User Request
     ↓
Agent Framework
     ↓
LLM + Tools
     ↓
Final Response

However, these frameworks hide many important implementation details.

To truly understand how agent systems work, it is useful to build a minimal agent runtime from scratch.

What Is an Agent Runtime?

An agent runtime is the system responsible for executing the agent’s reasoning loop.

Typical responsibilities include:

maintaining agent state
calling language models
invoking tools
processing observations
deciding when to terminate

Conceptually:

Agent Runtime
     ↓
observe → reason → act → reflect

This loop forms the core execution engine of the agent.

A Simplified Agent Loop

A minimal agent runtime often looks like this:

User Goal
   ↓
Generate reasoning step
   ↓
Select tool
   ↓
Execute tool
   ↓
Process observation
   ↓
Repeat until task complete

This cycle continues until the agent produces a final answer.

Why Learn the Internals?

Understanding the internals of agent runtimes offers several benefits.

Deeper Understanding

Building a runtime reveals how components such as:

planning
tool selection
memory
reasoning

interact with each other.

Debugging Complex Systems

Frameworks sometimes obscure the root cause of problems.

Example issue:

Agent fails to use correct tool

Without understanding the runtime, debugging this behavior can be difficult.

Custom Architectures

Real-world applications often require custom behavior.

Examples include:

specialized tool execution policies
custom memory architectures
domain-specific reasoning loops
optimized evaluation pipelines

Building your own runtime enables full control over system design.

Framework Limitations

Agent frameworks provide convenience, but they also introduce limitations.

Common issues include:

Abstraction Overhead

Frameworks may add additional layers of abstraction.

Example architecture:

Application
   ↓
Agent Framework
   ↓
Execution Engine
   ↓
LLM API

These layers can increase complexity and latency.

Limited Flexibility

Frameworks often assume a specific architecture.

Example:

ReAct loop

If you want to experiment with different reasoning strategies, the framework may impose constraints.

Hidden Behavior

Frameworks may perform internal operations that are not immediately visible.

Examples include:

implicit prompt modifications
automatic retries
hidden state management

These behaviors can make debugging difficult.

Performance Considerations

General-purpose frameworks must support many use cases.

This often results in additional overhead.

Example:

Framework abstraction → additional runtime cost

In high-performance systems, developers may prefer a lightweight custom runtime.

Minimal Runtimes vs Frameworks

The goal of building a minimal runtime is not necessarily to replace existing frameworks.

Instead, it helps developers understand the core mechanisms behind them.

Comparison:

Approach	Characteristics
framework-based	fast development
custom runtime	maximum control

Many teams combine both approaches.

The Core Components of an Agent Runtime

A minimal agent runtime usually includes several key components.

Component	Responsibility
agent loop	reasoning cycle
state manager	track agent context
tool dispatcher	execute tool calls
prompt generator	construct model prompts
termination logic	decide when to stop

Together, these components implement the agent execution process.

What We Will Build in This Module

In the next articles we will build a minimal agent runtime step by step.

The system will include:

an agent state machine
tool calling infrastructure
execution loop
debugging capabilities

The entire runtime will be implemented in roughly 300 lines of code.

This simplified implementation reveals the core architecture behind modern agent frameworks.

A Preview of the Runtime

The minimal runtime will follow a simple execution cycle.

Conceptually:

Goal
  ↓
LLM reasoning
  ↓
Tool call
  ↓
Observation
  ↓
State update
  ↓
Repeat

This loop forms the foundation of many agent frameworks.

Example Minimal Loop

Python
Rust

while not done:

    thought = llm.generate(prompt)

    action = parse_action(thought)

    observation = execute_tool(action)

    state.update(observation)

while !done {

    let thought = llm.generate(prompt);

    let action = parse_action(&thought);

    let observation = execute_tool(action);

    state.update(observation);
}

Although simple, this loop captures the essence of many modern agent systems.

Learning by Building

Building a runtime from scratch helps clarify how agent systems work internally.

Instead of relying on abstract frameworks, developers gain direct insight into:

reasoning loops
tool invocation
state management
termination conditions

This knowledge makes it easier to design custom agent architectures.

Looking Ahead

In this article we explored why developers sometimes build their own agent runtimes, focusing on understanding internals and overcoming framework limitations.

In the next article we will begin implementing the runtime by designing a simple agent state machine.

→ Continue to 11.2 — Designing a Simple Agent State Machine