You recorded a Loom walking through a bug. You clicked the broken button, showed the console error, hovered over the misaligned element, and narrated the whole thing. Now you want to hand that recording to Claude, Cursor, or ChatGPT and say “fix this.”

Can you? Partly. And the part that doesn't work is the part that matters most for a coding agent.

This post is a narrow one. It is not “Loom is bad” — Loom is a genuinely excellent product for humans watching async video, and we'll say so plainly below. This is about exactly one axis: what happens when the audience is a machine, specifically an AI coding agent that needs to act on what you recorded. On that axis, the design goals diverge, and it's worth understanding why before you assume a video-in, code-out workflow will just work.

TL;DR

  • Loom is built for humans, and it's very good at it — polished viewer experience, huge adoption, reliable sharing. None of that is in dispute here.
  • Loom's AI works on the transcript, not the pixels. Per Loom's own documentation, the AI provider (OpenAI) “receives transcript data as text files” and “does not receive video or audio content.” So the model reasons over what you said, not what you showed.
  • For a coding agent, what you showed is the whole point. Which button, which error string, which coordinate you clicked — that visual context is exactly what a transcript-only summary drops.
  • Loom's agentic “actions” live inside Atlassian. The Rovo agent workflows that turn a Loom into Jira tasks run in the Atlassian ecosystem (Jira, Confluence, Rovo), not as a portable feed your own coding agent can pull.
  • Clipy was built for this specific job. Append .md to any recording URL and any agent reads a summary, click-anchored key-moment frames with coordinates, and the transcript. Or connect the MCP server and let the agent pull context itself. Details, caveats, and the honest limits are below.

What Loom does well (this part isn't a knock)

Let's be fair before we get critical, because the critique only makes sense if you understand what Loom optimizes for.

Loom is one of the most polished async-video products ever built. The recorder is fast, the viewer experience is clean, playback is reliable, and the sharing model — paste a link, anyone can watch — is so smooth it became a verb inside a lot of teams. “Just Loom it” is shorthand for a reason. For a designer explaining a Figma flow, a PM walking through a spec, or a support rep showing a customer where to click, it's a great tool, and the adoption numbers reflect that.

Loom also ships real AI features. According to Loom's AI features documentation, auto-generated titles are available on all plans, and auto summaries, chapters, and “tasks” are available on the Business + AI and Enterprise tiers (applied to videos recorded after you purchase those plans). Under the hood, per that same documentation, Loom uses OpenAI's GPT models plus self-hosted open-source models such as Whisper for transcription. These features are genuinely useful for a human skimming a recording instead of watching all of it.

So the strengths are real: viewer polish, adoption, a solid transcript-plus-summary layer. The question is what those strengths are aimed at — and it's not an AI agent.

The AI-agent gap: transcript-only, and actions locked to Atlassian

Here's the crux. There are two distinct reasons a Loom recording is hard to hand to a coding agent, and they're worth separating.

1. The model gets the transcript, not the video

This is the big one, and it's not a criticism of Loom's privacy posture — it's actually a sensible privacy choice. Per Loom's own AI documentation, when its AI features run, “OpenAI receives transcript data as text files to generate titles, summaries, chapters, and text documents” and “OpenAI does not receive video or audio content.” Loom also notes the LLM providers don't retain inputs and outputs to improve their services. From a data-protection standpoint, keeping raw video and audio away from a third-party model is a reasonable thing to do.

But look at what that means for an agent. The AI is reasoning over a text transcript of what you narrated. It never sees the screen. So consider what a transcript actually captures from a typical bug walkthrough:

You say: “Okay, so I click this button here, and you can see the error pops up down at the bottom.”

The transcript captures those words faithfully. But “this button here” is a deictic reference — it points at something on screen that the transcript can't resolve. Which button? What did it say? Where was it? What was the exact error text at the bottom? An agent reading “I click this button here and the error pops up” has no idea. The information was in the pixels you showed, and the transcript-only pipeline never carried it.

This is fine for a human watching the video — the human sees the pixels. It's a problem for an agent that only gets text. The recording contained the answer; the format handed to the model threw it away.

2. The agentic “actions” are tied to the Atlassian ecosystem

Loom's AI story isn't only summaries. Since the Atlassian acquisition, Loom is woven into Atlassian's Rovo agent platform. In the Atlassian “Teamwork Collection,” a Rovo agent can, for example, take a Loom recording, extract action items, and create Jira tasks assigned to the right people. That's a legitimately useful workflow — if you live in Atlassian.

The catch, for our purposes, is that this agentic execution runs inside Atlassian's world (Jira, Confluence, Rovo Studio, the Teamwork Graph). It's not a portable, provider-neutral feed you can point your own Claude Code session, your Cursor project, or a custom LangGraph agent at. If your “agent” is Rovo creating Jira issues, great. If your agent is the coding assistant in your editor, Loom's agentic layer isn't reachable in the way you'd want — you're back to the transcript, with the pixel gap from point one.

So the gap is two-layered: the AI you can reach only sees text, and the richer agentic actions you might want are scoped to a specific ecosystem rather than exposed as something any coding agent can consume.

Why the pixels are the whole point for a coding agent

Step back and think about why you'd hand a recording to a coding agent at all. It's almost never to get a prose summary. It's to get the agent to do something: reproduce a bug, find the component, write the fix, add the guard.

For that, the agent needs the concrete, visual specifics that a walkthrough naturally contains:

  • The exact error text — not “an error appeared,” but the literal string, which the agent can grep for in the codebase.
  • Which element you interacted with — the label on the button, its position, the fact that you clicked it and not the one next to it.
  • The sequence of screens — what state the UI was in before and after each action, which is how a repro gets written.
  • Where your cursor actually was — because “the misaligned thing” is only meaningful if you know which thing.

All of that is visual. A transcript that says “I click here and it breaks” is, to an agent, almost content-free. The recording had everything; the pipeline lost it. That's the gap Clipy set out to close — not by being a “better Loom,” but by producing a recording format an agent can actually act on.

How Clipy handles it

Clipy is a free screen recorder, and on the human-facing surface it does the expected things: record your screen and camera, get a shareable link, no watermark, and viewers don't need to sign up. That part is table stakes.

The difference is that every Clipy recording is agent-readable, in two forms.

The .md endpoint

Take any recording's watch URL and append .md:

https://clipy.online/video/<id>.md

You get back a plain-Markdown document written for a model to read, not a human. It starts with a short note-for-agents preamble, then the title and an AI summary, and — this is the part a transcript can't give you — a key moments section. Each key moment is a timestamped entry with a JSON payload and an extracted frame from the video. Where Clipy can determine it, that entry includes:

  • a pointer crop — the region of the frame around the cursor,
  • click coordinates as fractions of the frame (so they're resolution-independent),
  • a source tag for how the moment was derived (fused, click, or hover) and a confidence value,
  • and a burned-in marker on the coordinate frames so the exact spot is visible in the image itself.

Then the full transcript. So an agent reading the .md gets what you said (transcript) and what you showed (frames plus click anchors) — which is exactly the visual context that the transcript-only path drops. If the recording was a feedback/review recording, the .md also surfaces a “Requested changes” / “Fix checklist” section built from the recording.

Two honest caveats, because overclaiming here would defeat the purpose:

  • Click coordinates are captured on the Mac app and on Chrome-extension recordings. The web recorder produces the summary and transcript but not click coordinates. If you specifically want click-anchored frames, record with the desktop app or the extension.
  • The .md is context, not a magic auto-fix. It gives your agent unusually good raw material — the frames, the coordinates, the transcript — but the agent still does the reasoning and writes the code. Clipy hands over the evidence; it doesn't claim to close the ticket for you.

The MCP server

If you'd rather your agent pull context itself instead of you pasting a URL, Clipy ships an MCP server. Run it with:

npx -y @clipy/mcp

Grab an API key from clipy.online/settings/api-keys, and your MCP-capable agent (Claude Code, Cursor, and other MCP clients) gets a read-only get_agent_context tool over your own recordings. It's read-only and scoped to recordings you own, and the .md it returns follows the same access rules as the watch page — so nothing gets exposed that the watch link wouldn't already show. Full setup lives in the MCP docs, and the broader agent story is on the Clipy for agents page.

Loom vs Clipy, on the narrow “feed it to a coding agent” axis

To keep this honest, this comparison is only about the agent-input use case. On the human async-video axis, Loom's polish and reach are strengths this list doesn't try to relitigate.

  • What the AI receives — Loom: transcript text only; per Loom's docs, the AI provider does not receive video or audio. Clipy: transcript plus extracted frames and click coordinates via the .md endpoint.
  • Visual/click context for the model — Loom: not carried into the AI layer (it's a transcript pipeline). Clipy: key-moment frames with fractional click coordinates and a burned-in marker (on Mac app + extension recordings).
  • How your own coding agent gets the data — Loom: agentic actions run inside the Atlassian/Rovo ecosystem (e.g. Loom → Jira tasks). Clipy: append .md to a URL, or connect the provider-neutral MCP server to any MCP-capable agent.
  • Where the richer AI actions live — Loom: Business + AI / Enterprise plans, and Rovo workflows in Atlassian. Clipy: the .md and MCP are the product's core, free, no watermark, no viewer signup.
  • Human viewer experience — Loom: excellent, widely adopted, very polished. Clipy: solid and shareable, but this is Loom's home turf, and we won't pretend otherwise.

The pattern: if the recording is for a person, Loom's strengths shine. If the recording is for a coding agent, you want the pixels and the coordinates to survive into the format the model reads — and that's the specific job Clipy was built for. There's a fuller, feature-by-feature breakdown in Clipy vs Loom (2026), and a broader migration overview on the Loom alternative page.

A realistic workflow with a coding agent

Here's what the Clipy path looks like end to end, so it's concrete rather than aspirational.

  1. You hit a bug. Instead of typing three paragraphs describing it, you record a 90-second Clipy with the Mac app or the Chrome extension — click the broken button, show the console, narrate what you expected.
  2. You paste the recording URL into your agent and add .md, or you point your MCP-connected agent at it with get_agent_context.
  3. The agent reads the summary, sees the key-moment frame where you clicked (with the coordinate and the burned-in marker), reads the exact error text from the transcript and the frame, and now has a concrete repro to work from.
  4. It writes the fix — or at minimum a focused, correct plan — because it saw what you showed, not just a lossy paraphrase of what you said.

The whole point is that recording is often faster than writing, and now the recording is legible to the agent instead of being a black box it can't open. If that idea is interesting, there's more on turning a recording into agent context in giving Claude, Cursor, and ChatGPT context from a screen recording and on the convert a screen recording to AI-ready Markdown writeup.

FAQ

Does Loom's AI actually watch my video?

No — and per Loom's documentation that's intentional. Loom's AI features operate on the transcript. The AI provider (OpenAI) “receives transcript data as text files” and “does not receive video or audio content,” according to Loom's AI features docs. It's a defensible privacy choice; it just means the AI never sees the pixels, which is what a coding agent usually needs.

Isn't the transcript enough to give an AI agent?

Sometimes, for narration-heavy explainers where nothing important is purely visual. But for bug reports and UI walkthroughs, the transcript loses the specifics — the exact error string, which element you clicked, the sequence of screens. Those live in the frames, not the words. That's why Clipy's .md carries frames and click coordinates alongside the transcript.

Can I feed an existing Loom to my coding agent?

You can get the transcript out of Loom and paste that in, and for some recordings that's genuinely useful. What you can't easily get is the visual/click context in a machine-readable form your agent can act on, and Loom's richer agentic actions are scoped to Atlassian's Rovo ecosystem rather than exposed as a portable feed. For an agent-first workflow, re-recording in a tool built for it is usually less work than reverse-engineering a video.

Do all Clipy recordings include click coordinates?

No. Click coordinates are captured on the Mac desktop app and on Chrome-extension recordings. The web recorder gives you the summary and transcript but not click coordinates. If click-anchored frames matter for your use case, record with the desktop app or the extension.

Does Clipy fix my bug automatically?

No, and we won't claim it does. Clipy produces the context — summary, key-moment frames with coordinates, transcript — in a format an agent can read. Your agent still does the reasoning and writes the code. Clipy's job is to make sure the agent has the evidence, not to pretend it's the one shipping the fix.

So is Loom bad?

Not at all, and that isn't the argument. Loom is excellent for humans watching async video, and its AI transcript/summary layer is useful for people skimming a recording. This post is about one narrow axis — handing a recording to an AI coding agent — where a transcript-only, Atlassian-scoped pipeline isn't built for the job, and Clipy's .md/MCP is. Different tools, different target audience for the output. If you want to see the agent path yourself, start at Clipy for agents or the for developers page.

If your recordings keep ending up in front of an AI, it's worth having the format match the reader. More on that theme in why an AI summary makes async video readable.