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🛠️ Make Your Agents Crash-Proof
⭐ 43.8k · langchain-ai/langgraphRepo
Most agent demos die the same way: the process crashes mid-task and you start over from scratch. That's the gap between a weekend project and something you'd trust with real work. LangGraph's durable execution closes it: the agent persists through failures and resumes exactly where it left off, no replay, no re-reasoning. Meanwhile the infrastructure a lot of us route every model call through just got a reminder that a gateway is also an attack surface. Both stories below are really the same lesson: the plumbing matters more than the model.
A low-level orchestration framework for building long-running, stateful agents.
An agent that dies mid-task normally means starting the whole run over, burning tokens and losing state. Durable execution lets it pick back up from the exact step it was on.
Quick start: pip install -U langgraph
Catch: it's a low-level framework, not a drag-and-drop builder. If you want something higher-level on top of it, LangChain ships Deep Agents for that.
HubSpotAffiliate
HubSpot Marketing Automation, visual workflows that send the email, set the task or move the deal when a contact does something. See how HubSpot works We may earn a commission.
LiteLLMMCP ⭐ 62k
One gateway for every major LLM provider, with built-in cost tracking and load balancing. Catch: version 1.107.0 shipped credential-stealing malware on October 21. It has been pulled and earlier versions are safe, so check what you have installed.
n8nRepo ⭐ 208.5k
A visual canvas for AI agent workflows, with code nodes for anything the UI can't express. Catch: the visual canvas is the draw, but production setups still lean on the code nodes once the workflow gets real.
hyperframesRepo ⭐ 72k
Write HTML, get a deterministic MP4. Built for coding agents, not manual editing. Catch: it's built around agent workflows first.
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The Agentic Engineering Playbook top engineers are using
Most engineers are stuck babysitting AI one prompt at a time. A handful have figured out how to build agents that plan, execute, and self-correct on their own. Sign up for The Code and get The Complete Playbook for Agentic Engineering. Claude Code, subagents, MCP, all of it.
New here? Start with The Awakening. It's free, it takes one sitting, and it's where every reader should begin.
Turn one angry email into a reply you can actually send
One prompt today instead of a how-to. It is one of ours, tested on ChatGPT and Claude. Copy it, paste it into the free app you already use, and fill in the five bits in capitals: what the customer actually said, what really happened on your end, which part of that is genuinely your fault, what you're authorized to offer, and what happens next with a real date attached.
The customer wrote:
[CUSTOMER MESSAGE]
What actually happened, from my side: [WHAT HAPPENED]
The part that is genuinely our fault: [OUR PART]
What I am allowed to offer: [WHAT I CAN OFFER]
What happens next and by when: [NEXT STEP]
Rules for the reply:
- Under 150 words.
- Open by naming the specific thing that went wrong for them, in their own words, before anything else. Not "we apologize for any inconvenience".
- Apologize once, for the part that is ours, and name that part out loud. Do not apologize four times and do not apologize for things that are not ours.
- No excuses. Do not blame a supplier, a system, or another department, even when it is true.
- If the part that is ours says "none", do not manufacture one. Acknowledge what they went through, say what is true, and still give them the next step.
- State what happens next as a fact with a date or a time on it, but only what I can actually commit to. If my next step is missing or uncertain, ask me rather than invent a promise.
- End without asking them to do anything, unless I actually need something from them.
Give me, in this order:
1. The reply, ready to send.
2. One line on the tone to use if I end up saying this on the phone instead.
3. The sentences in their message that carry the real complaint, quoted exactly, at most two. One is enough when that is all the message contains. People often lead with the wrong grievance and put the real one in the middle.
4. What I should not say back to this particular person, based on what they actually wrote.
If what I am allowed to offer clearly will not satisfy this customer, say so before the reply and tell me what I would have to go get approved.
The line that makes it work: "If the part that is ours says 'none', do not manufacture one. Acknowledge what they went through, say what is true, and still give them the next step." Most reply-drafting prompts push the model toward groveling because they assume fault exists somewhere. This one explicitly blocks that, so you get an honest reply instead of a manufactured apology, and it still forces a real next step even when you did nothing wrong.
TRY THIS: open your email, find the last customer message that made you wince, and paste it into the prompt right now. Two minutes gets you a draft you can send today, not a template you'll edit for twenty.
This is one of 100 in the Aigent prompt pack. The other 99, plus guided builds with Mike on video, are inside the Aigent membership: Join the Aigent membership. Seven days free, then $29 a month. One click to stop.
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LiteLLM 1.107.0 shipped credential-stealing malware. On October 21 an attacker pushed the compromised release to PyPI and Docker; LiteLLM says it has been pulled and earlier versions are safe. If you installed 1.107.0, rotate your provider keys and cloud credentials today, not tomorrow. (LiteLLM)
r/opensource banned AI-assisted projects outright. The new rules exclude anything "made by AI" or "intended for AI," and the backlash in the comments even spurred a rival sub, r/trueopensource. Worth watching if your open-source contributions lean on AI tooling. (The Verge AI)
Apollo's chief economist says AI has delivered zero financial payoff outside Big Tech. Profit margins and revenue per worker across the S&P 493 are flat despite the spend, which is a useful reality check against any vendor's ROI slide. (TechCrunch AI)
Vercel's CLI now ships spend management for agent-driven workloads. You can view and cap team budgets straight from the CLI, which matters once scripts and agents are the ones spending, not just people. (Vercel Blog)
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What do you spend on AI tools in a typical month?
Reply and tell me which one you're pulling this week, durable execution or the gateway patch. Both are the kind of unglamorous fix that saves you a 2am scramble later.
See you Tuesday.
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