Context, Harness, Loop: The Three Layers Behind an AI Agent Product

Let’s get one thing out of the way: every agent product is the same while loop at its core.

Claude Code, Manus, open-source projects like OpenClaw — strip the packaging and the bottom layer is identical: the model reads context → picks a tool → executes → results go back into context → repeat until done. The loop itself holds no secrets; you can write it in ten lines.

The real differences live in the three layers around the loop. Here’s a walkthrough, inside-out, using DeworkAI’s implementation.

Layer 1: Context Engineering — per-step quality

A model’s performance at each step depends on what it sees at that moment. Anthropic’s definition of context engineering nails it: find the smallest set of high-signal tokens that maximizes the outcome you want.

Start by defining the main agent: who it is, who it serves, what it explicitly does not do. Get the identity wrong and every layer above it tilts.

Then structure the system prompt in two blocks:

• Static: identity and principles, tool specs, safety boundaries, output format — the parts that never change across a session.
• Dynamic: long-term memory, MCP tool lists, user preferences, today’s date — the parts that drift over time.

Static-first ordering isn’t aesthetics — it’s money. LLM inference has prefix caching (the KV cache): identical prefixes don’t get recomputed, slashing cost and time-to-first-token. Put dynamic content early and you invalidate the cache for everything after it.

DeworkAI goes further and gets aggressive: the dynamic block is frozen per session too. At session start we snapshot memory and the tool list, then treat them as a stable prefix for the whole session — so nearly the entire system prompt is cacheable. The cost: mid-session memory updates aren’t visible until the next session. The payoff: maximal cache hit rates. For a product that has to control inference cost, this trade is the unit-economics lifeline.

Layer 2: Harness Engineering — the capability envelope

“Harness” is Anthropic’s word for the shell around Claude Code: everything outside the loop that lets an agent actually do work. Their post on harnesses for long-running agents is worth reading in full.

I design it by working backwards from the agent’s goal, in order:

1. Memory — what persists across sessions and how it’s retrieved.
2. Tools — which capabilities to grant, and a permission tier for each.
3. Environment & sandbox — what it can and cannot touch. DeworkAI made a counterintuitive call here: we removed bash. For non-engineer users, arbitrary command execution is unbounded risk; we replaced it with an action allowlist plus human-in-the-loop confirmation on anything irreversible. Slightly less capability, far more trust.
4. Subagents — big tasks fan out to sub-agents with clean, isolated context, run in parallel, then merge.
5. Skills — flows that ran once get harvested into reusable capabilities, recalled next time in a single sentence.

With this layer done, the agent becomes genuinely useful — but the hardest question is still open: is it reliable when nobody is watching?

Layer 3: Loop Engineering — unattended reliability

The term only got its name in June 2026 — Addy Osmani’s essay, and Claude Code creator Boris Cherny’s line: “I don’t prompt Claude anymore. I have loops that are running.” Stop prompting your agent turn by turn; design the loop that prompts it.

Why does this layer exist? Look at the architecture spectrum:

• Fixed pipelines (every step hard-coded): highly stable, but can’t generalize — one novel situation and they snap.
• Flexible agents (the model decides each step): smart, but every step is probabilistic — at 90% per-step accuracy, ten steps leaves you at 35%. Errors compound.

The core thesis of loop engineering: the verifier is the bottleneck, not the model. Generation is cheap and can run all day; whether any of that motion produces value is decided by the acceptance check.

DeworkAI’s answer is the Supervisor: while a workflow runs, an independent role oversees the whole thing — ruling pass/fail at every gate with a concrete reason, sending failures back with the reason attached, capping retries, and escalating to a human past the cap. Executor and verifier are separate roles — and Anthropic later reached the same conclusion in their own harness experiments: separating the agent doing the work from the agent judging it is one of the strongest reliability levers available.

This is also how flexibility and stability make peace: keep the agent smart, delegate reliability to loop design.

The layers stack — and you need all three

• Context sets per-step quality — the right things in view at every step.
• Harness sets the capability envelope — what it can and cannot do.
• Loop sets thousand-step reliability — nothing breaks when nobody’s watching.

The industry one-liner sums it up: prompt engineering makes a model smart for one turn; loop engineering makes a system reliable for a thousand turns. An agent demo only needs layer one. An agent product pays for all three.

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DeworkAI is these three layers, shipped — watch the one-minute demo, or start free and hand over your first task.