u/Downtown_Item_9996

A clean demo can still fail on replay. Require 3 replay tests before inviting users.

1. Restart the session. The agent should rebuild context from source data, not chat history.
2. Revoke one integration permission. The error should name the blocked step, not claim success.
3. Repeat the same request. The second run must not duplicate a public write.

Save the input, tool calls, visible output, and recovery path. Pass only when a user can verify the result; a “done” flag is not proof.

I work on Runbear, where we build agent workflows. What replay failure has been hardest for your app?

An agent demo fails if it cannot prove 3 states: the trigger, the tool call, and the user-visible result.

A practical teardown:

1. Start with the exact event that wakes the agent.
2. Show which system it can read or change, and the permission boundary.
3. Force one failure, then show retry, approval, or rollback behavior.
4. End on the destination users inspect, not an internal ‘completed’ badge.

This separates a working workflow from a polished chat recording. I work on Runbear, which connects AI agents to team tools. What other failure case should be mandatory in a test?

A passing demo can hide 3 failures.

For an agent integration, test more than whether it answers once: disconnect the tool mid-run, revoke its credential, then repeat the request after reconnecting. Check whether the user sees the failed step, retry duplicates the action, and the thread preserves context. A useful pass condition is one visible error, zero duplicate writes, and a successful retry without restarting the conversation.

I work on Runbear, a tool for running AI agents in team chat. Does this failure-first checklist match how you test integrations?

If an agent demo fails any of these 3 checks, the result is not ready for outside testers.

1. Repeatability: run the same task three times and note where outputs diverge.
2. Recovery: remove one permission or input and require a specific next step, not a silent failure.
3. Evidence: save the prompt, tool calls, output, and elapsed time in one trace.

Ask testers to label only the first broken step. That turns “it felt unreliable” into a reproducible report. I work on Runbear, where we build tools for running agents in team workflows.

A one-shot agent demo proves almost nothing.

Before adopting an agent or MCP workflow tool, test five failure paths: expired OAuth, missing tool permission, malformed tool output, a retry after partial success, and a duplicate trigger. For each one, record whether the run stops safely, preserves context, and tells an operator what to do next. Then repeat the same workflow three times and compare the visible outcome, not the model's confidence. I work on Runbear, so this is also the checklist I use when evaluating agent workflow products. What failure case would you add?

A tool test fails when it produces fewer than 3 artifacts: the exact input, the tool-call trace, and the final output.

For an AI agent or MCP app, run one normal case and two failure cases: missing permission and malformed input. Record where the failure appears, whether the user sees a useful recovery step, and whether retrying can duplicate an action. A polished happy path cannot answer those questions.

I work on Runbear, which helps teams run AI agents in Slack. I’m posting this to get sharper feedback on the test boundary, not to replace a real test.

If an agent demo needs 3 manual fixes, it is not ready for a recurring workflow.

Run 5 checks: repeat the same task; revoke one permission; feed an empty result; restart the conversation; inspect the final action in the destination app. Score pass/fail. A useful pilot should clear 4 of 5 without hidden cleanup. Every failure should leave an observable state, not a confident success message.

I work on Runbear, where we build infrastructure for running agents in team tools. This checklist is product-agnostic: test repeatability and read-back, not demo quality.

Two failures in five runs is enough to stop a beta.

Test the workflow, not the best-case demo: run the same task five times with one expired credential, one missing field, and one ambiguous request. Record whether the agent detects the fault, asks for the right input, avoids a duplicate side effect, and leaves a readable audit trail. A pass should require five outcomes and zero silent writes. If recovery depends on the builder watching logs, users are still the runtime.

I work on Runbear, an agent workflow product. This is the checklist I’d use before asking strangers to test any agent.

A Slack agent can pass 10/10 happy-path prompts and still fail the first real handoff.

Test three things: (1) trigger reliability, does the same mention start the workflow? (2) state integrity, does thread context survive a restart? (3) recovery, does a tool timeout produce a useful next step? For each, record expected output, actual output, and recovery behavior. If diagnosis requires admin access, treat that as a launch blocker, not an edge case.

I work on Runbear, which helps teams run AI agents in workplace tools. This checklist has no link so it stands on its own.

에이전트 앱 테스트에서는 성공률 100%보다 실패 3종을 재현할 수 있는지가 먼저입니다.

먼저 같은 입력을 정상 응답, 권한 거부, 타임아웃 조건으로 각각 실행하세요. 각 실행에서 사용자가 본 상태, 실제 도구 호출, 재시도 여부를 한 줄씩 기록하면 UI 문제와 연동 문제를 분리할 수 있습니다. 다음으로 중간 단계에서 브라우저나 API를 끊고, 앱이 멈췄는지 실패를 설명했는지 확인하세요. 마지막으로 같은 요청을 두 번 보내 중복 게시나 중복 티켓이 생기지 않는지 봅니다.

판정 기준은 단순합니다. 실패 위치가 사용자에게 보이고, 재시도가 같은 부작용을 만들지 않으며, 운영자가 원인을 로그에서 찾을 수 있어야 합니다. 이 세 조건 중 하나라도 빠지면 데모는 통과해도 실제 운영 테스트는 실패입니다.

I work on Runbear. Runbear는 팀 채팅에서 에이전트와 MCP 도구를 연결해 실제 업무 흐름을 실행하는 제품입니다. 링크 없이 이 체크리스트 자체에 대한 반례나 빠진 실패 조건을 듣고 싶습니다.

We built BearDrive because our agents were producing more useful files than our team could keep track of.

HTML dashboards, PDFs, Markdown docs, research notes, and launch materials ended up scattered across laptops and Slack. A week later, finding the file was often harder than creating it.

BearDrive connects a local folder to a shared project and installs hooks for coding agents such as Claude Code and Codex. The hooks pull before prompts and push after edits, so the next teammate’s agent can find the current file without a manual Slack or Drive handoff.

The core is open source and self-hostable. The hosted version is free during beta.

We launched on Product Hunt today: https://www.producthunt.com/products/beardrive?launch=beardrive

GitHub: https://github.com/runbear-io/beardrive

https://preview.redd.it/a2de31u08yih1.png?width=1564&format=png&auto=webp&s=22da15f54adbd28ef2b52c07f59a579ebb043c9f

BearDrive keeps files made and used by coding agents in sync across teammates and machines.

We built it after watching useful HTML dashboards, PDFs, Markdown docs, and research notes pile up on laptops and in Slack. Making the files was easy. finding the current copy later was not.

Instead of asking everyone to remember another sync command, BearDrive lets the coding agent handle setup and registers hooks around its normal work. Shared files are updated before prompts and after edits.

The core is open source and self-hostable. The hosted version is free during beta.

We launched today: https://www.producthunt.com/products/beardrive?launch=beardrive

We launched BearDrive on Product Hunt today.

It came from a pretty mundane problem. Our agents make HTML dashboards, PDFs, research notes, and launch docs, but the files end up scattered across laptops and Slack. When someone needs a report later, the annoying part is figuring out where it is and which copy is current.

BearDrive connects a local folder to a shared project and adds hooks to coding agents like Claude Code and Codex. The hooks pull before prompts and push after edits, so the shared files don't depend on everyone remembering a separate sync step.

Here's the launch: https://www.producthunt.com/products/beardrive?launch=beardrive

We're using "a Dropbox-like shared folder for AI agent files" to explain it. Does that make sense on first read, or would you frame the problem differently?

Our agents make HTML dashboards, PDFs, research notes, and launch docs all day.

Then a report ends up on someone's laptop or in Slack. A week later, we know it exists, but not where it is or whether final-v2-FINAL.html is actually final.

That's when the "Dropbox moment for AI agents" clicked for me. Making files is easy now; handing them to the next person or agent is not.

BearDrive is our attempt at fixing that: a normal shared folder agents can read and write, with links and version history.

Open source, and launched today:

https://www.producthunt.com/products/beardrive?launch=beardrive

How are you handling this?

Disclosure: I'm one of the makers.

Spent today fixing an agent that drives a browser for me, and every bug had the same shape: the tool reported success and nothing happened.

Four that cost me the most time.

The submit click returned ok and the composer emptied, so my read-back said "posted". The post was never created. Reddit had put a reCAPTCHA on the form and the click just did nothing useful. I now probe for the captcha element before typing instead of guessing from a failed read-back.

Media upload returned success and the attachment count was still 0. The upload API resolves before the composer hydrates the preview, so submitting right after it posts text with no image. Waiting for a visible attachment node fixed it.

Read-back comparison kept failing on identical text. The editor normalizes trailing newlines away, so my "typed matches approved copy" check compared a string against itself minus a newline and rejected it.

And `wait --fn` in the CLI I was using dropped the session to about:blank, so the next command ran against a blank page and returned an empty result that looked like "no items found". Replaced that whole path with the underlying library directly.

The pattern I keep hitting: the failure modes that hurt aren't crashes, they're operations that return a success value while the side effect never lands. Empty result and broken collector look identical downstream.

What do you assert after a tool call to prove the effect actually happened, not just that the call returned?