Agent Runtime Basics: Models, Loops, and State

Agent Runtime Basics: Models, Loops, and State

Before you reach for frameworks, you need to understand the runtime — the system that decides when the agent thinks, when it acts, and how it tracks state.

A reliable runtime is not complicated, but it is intentional. Most failures come from missing state, unclear stop conditions, or tool calls without supervision.

The Minimum Viable Runtime

Every agent runtime needs four pieces:

1. A model — the reasoning engine (LLM)
2. State — session context, inputs, and outputs
3. Tools — actions the agent can take
4. A loop — rules for how thinking and acting alternate

If any of these are missing, you don’t have an agent — you have a chatbot.

A Simple Loop

A minimal loop looks like:

input → plan → act → observe → decide next step

The loop repeats until a stop condition is met (goal achieved, budget reached, or human approval required).

State: The Quiet Failure Point

State is what makes an agent reliable over time. You must decide:

• What to keep: intent, constraints, prior tool results
• What to reset: speculative reasoning, irrelevant context
• Where it lives: in-memory, database, or cache

If state is unclear, the agent will drift, repeat itself, or hallucinate.

Sync vs Async Execution

• Sync loops are great for short tasks and UI interactions.
• Async loops are better for long-running workflows, queues, and background jobs.

Choose based on latency tolerance and the cost of failure.

🧠 Knowledge Check

Q: What are the four minimum components of an agent runtime?

A model, state, tools, and a loop that defines how the agent thinks and acts.