BlogAgent Architecture
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AI agent architecture: the loop, the harness and the runtime
AI agent architecture is the structure of a production agent: the loop that perceives, reasons and acts; the harness of tools, context and guardrails designed around the model; and the runtime that executes, evaluates, versions and traces the whole thing. The model supplies capability. The architecture decides how much of that capability becomes dependable behaviour.
A demo agent needs three things: a loop, a model and a few tools. A production agent needs everything around the loop, which is where the sprints actually go: termination conditions, typed tool envelopes, context that does not grow without bound, evaluation before a change ships, versions you can diff and roll back, and a stable interface the application calls. The articles below take that structure apart layer by layer, from what an agent loop is and why it fails, through harness engineering and orchestration, to what an agent runtime owns so you do not have to rebuild the shell for every agent you ship.
The figures that make the case: tool-calling fails 3–15% of the time in production, depending on model size and task complexity, and the most damaging failures are silent. Gartner found the token consumption gap between pilot chatbots and production agent workflows sits at 5–30×, because an agent re-sends its accumulated context at every step. And on the upside of getting the structure right, Anthropic's multi-agent research system found that parallel subagents and parallel tool calls cut research time by up to 90%.
Articles on AI agent architecture (8)
No-Code Agent Builder: What Goes Wrong
No-code agent builders ship fast but skip prompt versioning, CI eval gates, and model fallback. Learn what breaks in production and how to close the gap.
· 5 min read
What Is an Embodied Agent?
An embodied agent observes a live environment and acts to change it. What the perception-action loop means for production reliability, cost, and CI/CD.
· 7 min read
What Is Agent Orchestration?
Agent orchestration is the control layer that decides which AI agents run, in what order, with what context, and how the system recovers when any step fails.
· 5 min read
Agent Loop: Architecture and Failure Modes
An agent loop is the perceive-plan-act cycle at the core of every AI agent. Architecture, two dominant patterns, and four production failure modes.
· 6 min read
What is an agent runtime?
An agent runtime is the hosted layer that runs AI agents in production — executing the loop, measuring each version, and exposing it as a callable function.
· 6 min read
What is harness engineering?
Harness engineering is the discipline of building the scaffolding that turns a model into a dependable agent — tools, context, evals, and guardrails.
· 6 min read
AI Agent Hosting: Host an Agent as a Function
AI agent hosting done right: host the agent as a typed, versioned function on an LLM ops platform instead of calling the model API from your app.
· 6 min read
The Eight Pillars of a Production-Grade Agent
A production-grade agent is eight operational pillars wide. What they are, and how an LLM ops platform provides that shell so you don't rebuild it per agent.
· 7 min read
FAQ
- What is an AI agent architecture?
- An AI agent architecture is the structure of a production agent: the loop that perceives, reasons and acts; the harness of tools, context management and guardrails designed around the model; and the runtime that executes the loop, evaluates each version, versions and traces it, and exposes it to the application as a callable function.
- What is the difference between an agent framework and an agent runtime?
- An agent framework is a library you embed in your application to write an agent; you still own hosting, evaluation and rollback. An agent runtime is the hosted layer on the other side of the deploy boundary that runs the agent and owns that operational surface. They compose: build with a framework, run the result on a runtime.
- What is an agent harness?
- The harness is everything between the model and the world: the task framing, the tools and their interfaces, the strategy for what enters the context window, the guardrails on inputs and outputs, and the evaluation loop that measures whether a change helped. It is designed per agent; the runtime is shared across agents.
- When does a single agent need orchestration?
- When one agent's context, tool set or latency budget can no longer hold the whole task: the orchestration layer decides which agents run, in what order, with what context, and how the system recovers when a step fails. Most teams reach for it later than they expect, and most production failures come from a single agent's loop before they come from orchestration.
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