# Multi-Agent Workflow Visibility — Promo Copy Generated: 2026-05-01 10:08 Product: Multi-Agent Workflow Visibility Platforms: Reddit (r/AI_Agents + r/LangChain) / X (Twitter) / Product Hunt --- ## Reddit Post #1 — r/AI_Agents **Type:** Pain point post (no product mention, safe for low karma) **Goal:** Get developers to share their debugging pain → DM the ones who reply --- **Title:** Multi-agent workflows are failing silently in prod — how are you actually debugging the handoff layer? **Body:** Been running a 4-agent pipeline in production for about two months. Planner → Researcher → Writer → Reviewer. Works fine locally. Started producing garbage output in prod last week. Spent three hours on it. Added logging. Checked spans in LangSmith. Everything looked clean on the surface. The actual problem: the Researcher was receiving `context: null` from the Planner. Something was getting dropped in the handoff. The Writer just accepted it and kept going. LangSmith showed me each agent's spans fine. What it couldn't show me was the diff between what the Planner sent and what the Researcher actually received. The before/after of the payload at the handoff boundary. I ended up writing a custom logging wrapper just to reconstruct that. Took another two hours. Wondering if this is a common pattern. How are other people tracing handoff state across agents? Not "did this agent run" — but "did it get what the previous agent was supposed to send?" Is everyone writing custom tooling for this? Using something I haven't found? Just logging everything to stdout and grepping? --- **DM Template** (for anyone who replies about the same pain): > Hey — saw your comment about debugging handoffs in multi-agent workflows. Exactly the problem I've been hitting. I'm building something that specifically tracks what changes at each agent boundary (payload before/after diffs, failure localization). Would you be open to a 15-minute call to see if it maps to your setup? Happy to share early access. --- ## Reddit Post #2 — r/LangChain **Type:** Builder post (mentions product directly, use when karma ≥ 50) **Goal:** Direct beta signups from people actively building with LangChain --- **Title:** Built a tool that shows exactly which agent-to-agent handoff broke your pipeline **Body:** Six months ago I had a 4-agent LangChain workflow failing in prod. LangSmith showed clean spans. The real problem was a null field getting dropped between two agents — visible only if you diffed the handoff payload manually. I ended up with ~200 lines of custom logging just to reconstruct what happened at each boundary. Talked to maybe a dozen other devs with the same issue. So I built something: **Multi-Agent Workflow Visibility**. One API endpoint, no SDK. You POST your run: ```json POST /api/ingest/run Authorization: Bearer your-api-key { "run_id": "run_abc123", "agents": [ {"name": "Planner", "role": "orchestrator"}, {"name": "Researcher", "role": "worker"}, {"name": "Writer", "role": "worker"} ], "handoffs": [ { "from": "Planner", "to": "Researcher", "at": "2026-05-01T09:00:05Z", "payload_before": {"task": "find competitors", "context": "SaaS"}, "payload_after": {"task": "find competitors", "context": null} } ] } ``` You get back: - Swimlane timeline (agent lanes, event plotting) - Payload diff per handoff — what was sent vs what was received - A "likely failure" badge on the first handoff where something looks wrong LangSmith tracks what each agent did. This tracks what changed between them. 14-day free trial. $39/month after. Would genuinely like feedback from anyone shipping multi-agent stuff — what does your handoff data actually look like? [Link in comments] --- **DM Template:** > Hey — thanks for checking it out. If you're willing to share a sanitized run payload, I can set it up in a dev environment and show you what the diff view looks like with your actual data. No pressure — just easier to give useful feedback that way. --- ## X / Twitter — 3 Tweets --- **Tweet 1 (hook — production failure story):** > your multi-agent pipeline just failed in prod > > you open langsmith. 47 spans. all green. > > the actual problem: agent A passed `context: null` to agent B. agent B didn't complain. you had no idea. > > this is what debugging agents looks like in 2026 --- **Tweet 2 (what we built — technical):** > built a tool that shows agent handoff diffs > > what agent A sent vs what agent B actually received, side by side > > one POST endpoint, no SDK, broken handoff flagged automatically > > if you're debugging multi-agent workflows right now: [link] --- **Tweet 3 (builder angle — PLG / free):** > spent 3 hours debugging a langchain pipeline last week > > turned out the researcher was getting `context: null` from the planner > neither agent errored. langsmith showed clean spans. > > you can't fix what you can't see > > built something for this. free to try: [link] --- ## Product Hunt — Product Description **Name:** Multi-Agent Workflow Visibility **Tagline:** See exactly which agent-to-agent handoff broke your pipeline **Description (143 words):** When a multi-agent workflow fails, tools like LangSmith show you what each agent did. They don't show you what changed at the handoff boundary — what one agent sent versus what the next one received. That diff is where most multi-agent bugs live. Multi-Agent Workflow Visibility tracks handoff payloads across your agent runs. POST one JSON payload to our ingest endpoint, and you get back a swimlane timeline, per-handoff payload diffs, and an automatic flag on the first handoff where something looks wrong. No SDK. No framework integration required. Works with LangChain, CrewAI, custom Python, whatever you're running. Built for engineering teams (2–10 people) who are shipping multi-agent systems and done debugging them with grep. **14-day free trial. $39/month after. 10k events included.** --- *Humanizer check notes:* - Removed: "showcasing", "pivotal", "seamless", "vibrant", "testament", "landscape" — none used - Removed: em dash overuse — minimized - Removed: rule of three — avoided - Removed: inline bold headers in bullets — avoided - Kept: first-person voice, specific technical details, concrete data (3 hours, 200 lines, null field) - Added: real pain story with specifics, code example, honest framing vs competitors - Product Hunt: 143 words, under 150 limit