Most writing about agentic coding tools falls into two camps: breathless demos that skip the failure modes, or dismissive takes that never engage with what actually works. An "autopsy" framing promises the third thing — a careful post-mortem of how Claude Code performs when pushed into deep research territory, written by people at Steel (a browser-infrastructure-for-agents company who have skin in the game on agent reliability).

Based on the title and source, the piece almost certainly digs into:

• Where the agent loop breaks down on multi-hour research tasks — context window exhaustion, tool-call drift, the moment when the agent stops verifying claims and starts confabulating sources.
• The gap between "coding agent" and "research agent". Claude Code is optimized for filesystem + shell + edit loops. Asking it to do deep web research stresses different muscles: source triangulation, citation hygiene, knowing when to stop searching. The autopsy framing suggests the author found specific failure modes worth naming.
• Browser tooling and sandboxing — Steel's whole product is giving agents a real browser, so expect concrete observations about what headless fetching misses vs. a full browser session, and how Claude Code's default web tools compare.
• Token economics and planning horizon. Deep research burns tokens fast. There's likely a discussion of how much compute it took, and whether the output justified the spend.

Why this matters for a technical audience: every team currently evaluating "should we build on top of Claude Code or roll our own agent harness?" is making this decision with vibes, not data. A detailed write-up of where a popular agent harness actually breaks on a non-trivial task is exactly the kind of artifact that helps people choose. It also lands at a moment when "deep research" has become a marketing term across every frontier lab's product line — independent evaluations are scarce and valuable.

The post-mortem genre is underrated in this space. We get plenty of launch posts and benchmark charts, but very little patient analysis of why a long-horizon run went sideways. Those are the writeups that change how practitioners build.