Can an AI help you anticipate the chain reactions you never see coming? That’s the question at the heart of this episode, sparked by a listener who’s been thinking about cascading failures — those doom loops where one small breakdown eats your margin and triggers a freefall. The answer is a four-agent system built with today’s LLM tools. The Ideation Agent uses structured decomposition templates to generate plausible failure chains grounded in the user’s specific context — home setup, work dependencies, financial buffer. Drawing on Anthropic’s Causal Chain Decomposition paper, which showed LLMs can identify chains humans miss 28% of the time, this agent runs at high temperature for creative but constrained scenarios. The Interviewer Agent then presents the scenario and records the user’s response, forcing active engagement rather than passive reading. The Critique Agent — the hardest to get right — analyzes the response for gaps, overconfidence, and missing dependencies. A MIT Media Lab study found that adversarial feedback framed as collaborative improves engagement by 40%, so the persona is “skeptical but supportive colleague.” Borrowing from IQT Labs’ Snowglobe framework, this agent focuses on finding the keystone assumption: the single failure that collapses the entire plan. Finally, the Remediation Agent synthesizes everything into an actionable prevention plan. The architecture demands separate agents with different temperature settings — high for ideation, low for critique, medium for interviewing — because one agent averaging everything produces mediocrity. For implementation, LangGraph’s state machine enforces the strict sequence; CrewAI’s role-based approach works better for parallel tasks; AutoGen handles dynamic conversation patterns. The result is a system that doesn’t just list risks but forces you to confront what you haven’t thought of.
#4347: Building an AI That Finds Your Blind Spots
Can LLMs surface the chain reactions you never see? A four-agent system for proactive preparedness.
Episode Details
- Episode ID
- MWP-4526
- Published
- Duration
- 28:48
- Audio
- Direct link
- Pipeline
- V5
- TTS Engine
-
chatterbox-regular - Script Writing Agent
- deepseek-v4-pro
AI-Generated Content: This podcast is created using AI personas. Please verify any important information independently.
Downloads
Transcript (TXT)
Plain text transcript file
Transcript (PDF)
Formatted PDF with styling
You Might Also Like
Never miss an episode
New episodes drop daily — subscribe on your favorite platform
New to the show? Start here#4347: Building an AI That Finds Your Blind Spots
Daniel sent us this one — and it's one of those prompts that feels like it grew directly out of the last twelve months of his life. He's been thinking about cascading failures, those doom loops where one thing goes wrong and eats your margin, which makes the next thing go wrong, and suddenly you're in freefall. His question is: can we use AI not just to answer questions, but to actively surface our blind spots — to build a multi-agent system that generates cascading failure scenarios, interviews you about how you'd respond, critiques your answers, and then documents a preventative action plan. He wants to know how you'd actually build this thing with the tools available right now.
This is exactly the kind of thing I've been wanting to dig into. The last year has been a masterclass in cascading failures — wars, housing chaos, the slow realization that you can't possibly prep for every scenario. But what you can do is prep for the process of failing gracefully. And that's what Daniel's really asking about here. Not a checklist for earthquakes or backup generators. A system that forces you to confront what you haven't thought of.
Which is the whole problem with traditional preparedness, right? You buy the earthquake kit, you have a spare tire, you back up your drive. All of that assumes you know what's coming. The thing that actually gets you is the chain reaction you didn't see — the internet goes down, so you work from a coffee shop, so your laptop gets stolen, so you lose client data, so you miss a deadline, so you lose the client. None of those individual steps was unforeseeable, but the chain wasn't obvious until it happened.
That's where LLMs are genuinely interesting for this use case. They're pattern-matchers on an enormous scale. They can simulate chains of causation that humans don't naturally see because we're too close to our own assumptions. There was a paper from Anthropic back in January on what they called Causal Chain Decomposition in LLMs — they showed that when you give these models a structured decomposition template, their accuracy on multi-step causal chains improves by thirty-four percent over free-form prompting. That's not marginal. That's the difference between a system that tells you obvious things and one that actually surprises you.
Thirty-four percent is a lot of surprise. So let's step back and ask: what exactly are we trying to solve here?
The core problem is that human beings are terrible at anticipating cascading failures. We have availability bias — we over-weight risks we've experienced or heard about recently. We anchor on the first scenario we think of and don't explore the space. An LLM doesn't have those constraints. It can generate chains that feel novel because it's not filtering through personal experience.
The other piece Daniel flagged is that this isn't just about identifying risks — it's about forcing the user to articulate and defend their response, then having that response critiqued. Which is a very different thing from reading a list of things that could go wrong and nodding along.
Reading a list is passive. Being interviewed, having to say out loud what you'd actually do, and then having someone — or something — poke holes in it, that's active. It's the difference between watching a workout video and having a personal trainer watch your form.
Or between reading about archery and having someone tell you your elbow's too high.
I do know about that one. Alright, let's get into the architecture. Daniel laid out four agents. Walk me through them.
The stack looks like this. Agent one is the Ideation Agent. Its job is to generate cascading failure scenarios based on the user's specific context — where they live, what they do for work, their financial margin, their dependencies. Agent two is the Interviewer Agent. It presents the scenario to the user, asks "what do you do?", and records the response. Agent three is the Critique Agent — this is the hardest one to get right. It analyzes the user's response for gaps, overconfidence, logical fallacies, and missing dependencies. And agent four is the Remediation Agent, which synthesizes the critique into a documented preventative action plan with specific, actionable steps.
Ideate, interview, critique, remediate. Let's start with the Ideation Agent. How do you prompt an LLM to generate something that isn't just the obvious stuff?
This is where that Anthropic paper gets really practical. They found that the key is structured decomposition templates. Instead of saying "give me a disaster scenario," you give the model a causal chain structure. Something like: "Given the user's context — home setup, business dependencies, geographic location, financial margin — generate a scenario where failure A triggers failure B, which then makes failure C inevitable. Explain the causal mechanism at each step." That structure forces the model to think in chains rather than isolated events.
You're constraining the form, not the content.
The form is rigid — A leads to B leads to C, explain the mechanism — but the content space is wide open. And you want to run this at a relatively high temperature, maybe zero point eight or zero point nine, because you want creative, surprising chains. The paper showed that LLMs identified causal chains that human experts missed in twenty-eight percent of test cases. That's the blind spot discovery rate.
Twenty-eight percent of test cases where the model saw something the experts didn't. That's substantial. But I'm already thinking about the failure mode here — what stops the Ideation Agent from generating absurd, Hollywood-style scenarios that nobody would take seriously?
That's a real risk, and the mitigation is grounding. You give the agent the user's actual context — not just "freelancer in a city," but specifics about their setup, their clients, their backup habits, their financial buffer. The more grounded the context, the harder it is for the model to drift into nonsense. You also constrain the prompt to require plausible causal mechanisms. If the agent says "an asteroid hits your apartment," it has to explain how that's a plausible failure chain given the user's context. And if it can't, the chain gets rejected.
The plausibility check is built into the prompt structure itself.
Now let's talk about agent three, the Critique Agent, because this is where most implementations fall apart. The Critique Agent has to be adversarial but constructive. Too harsh, and the user shuts down. Too soft, and it misses real gaps. There's a really interesting study from the MIT Media Lab in twenty twenty-five on what they called AI as Coach — they found that adversarial feedback framed as collaborative improved user engagement by forty percent compared to direct criticism.
Forty percent is enormous for something as simple as how you phrase the feedback.
It's huge. And the specific phrasing they tested was things like "let me share what my colleague noticed" versus "here's what you got wrong." Same feedback, different wrapper, forty percent difference in engagement. So for the Critique Agent, the persona prompt matters enormously. You don't want "you are a harsh critic who will tear apart the user's plan." You want something like "you are a skeptical but supportive colleague. Your goal is to make the user's plan stronger. Identify assumptions they're making that could be wrong, explain what would happen if those assumptions fail, and propose a more robust alternative.
"Skeptical but supportive colleague" is a very specific persona. It's not a friend who lets things slide, and it's not an adversary trying to win.
There's a precedent for this in the adversarial AI space that's directly applicable. Do you remember the Snowglobe framework from IQT Labs?
The wargaming thing that got archived?
That's the one. Shipped to version one point oh in September twenty twenty-five, open source, then archived. But its design patterns are gold. Snowglobe's key innovation was a separate adversary agent that was explicitly prompted to find the weakest link in a plan and attack it. The adversary was given the user's full context and instructed to identify the single assumption whose failure would cause the entire plan to collapse. That's a very specific, very powerful prompt pattern — it's not "find everything wrong," it's "find the keystone assumption.
Find the one thing that, if it breaks, everything breaks.
And that's much more useful than a laundry list of criticisms. If you tell someone twenty things they did wrong, they'll remember none of them. If you tell them "your entire plan assumes you can get online within four hours — what if you can't?
It also forces the Critique Agent to prioritize, which is a hard thing for LLMs to do well. They tend to want to be comprehensive.
Comprehensive is the enemy of actionable. Snowglobe's failure analysis actually showed something important here — single-agent systems produced forty percent fewer actionable critiques than multi-agent setups. When you try to do everything in one agent, you get a muddled middle ground. The creativity of the ideation bleeds into the precision of the critique, and you end up with scenarios that are kind of interesting and critiques that are kind of useful, but nothing that's sharp.
Which brings us to the architecture question. Daniel asked about this directly — should this be separate agents or one agent with tool use and memory?
Separate agents, shared context window. And the reason is temperature. The Ideation Agent needs high temperature — you want creative, surprising chains. The Critique Agent needs low temperature — you want precision and consistency. The Interviewer Agent needs medium temperature with strong instruction-following — it has to stay on script and be empathetic. If you try to do all of that in a single agent with a single temperature setting, you average everything into mediocrity. It's like trying to use the same oven temperature for a soufflé and a brisket.
That's a very Herman way to put it.
I stand by it. Each agent has a distinct cognitive style, and the temperature parameter is how you encode that style. The Snowglobe team understood this — their adversary agent ran at a different temperature than their scenario generator, and that separation was part of why their critiques were sharper.
We've got the agents. Now let's talk about how you actually wire them together, and what happens when real people sit down to use this thing.
The implementation question. It's July twenty twenty-six. What tools do you actually use to build this? There are three major frameworks worth talking about. LangGraph, CrewAI, and AutoGen.
Give me the compare and contrast.
LangGraph is built for stateful graph-based agent orchestration. You define nodes for each agent, and conditional edges that control the flow between them. The key feature for our use case is that LangGraph lets you enforce a strict state machine — you can't skip from ideation to remediation without going through critique. The sequence is baked into the graph structure.
Which matters because the sequence is the whole point.
CrewAI takes a different approach — it's role-based. You define agent personas with specific goals and backstories, and then you give them tasks. It's better for parallel agent work, where multiple agents are operating simultaneously and coordinating. But our use case is strictly sequential. Ideate, then interview, then critique, then remediate. There's no parallelism here. And then there's AutoGen, which is Microsoft's framework — it's built for multi-agent conversation patterns, with dynamic agent discovery and turn-taking. It's powerful, but it's also more complex than we need. For a four-agent sequential pipeline, LangGraph's state machine approach is cleaner.
LangGraph with a state machine that tracks which phase the simulation is in.
That's my recommendation. You define four nodes — ideation, interview, critique, remediation — and you wire them with conditional edges that enforce the sequence. The state object carries the user's context, the generated scenario, the user's response, the critique, and the final action plan. Each node reads from the state and writes back to it.
What about memory across sessions? If I use this system once a month, I don't want it to generate the same scenario every time.
This is where a vector store comes in — something like ChromaDB or Pinecone. After each session, you embed the generated scenario and the critique and store them. At the start of the next session, the Ideation Agent retrieves similar past scenarios and is explicitly prompted to avoid repeating them. You also embed the user's previous responses and critiques, so the Critique Agent can reference them — "last time we discussed your backup strategy, you said you'd implement offsite backups.
Which closes the loop. It turns a one-time exercise into a continuous improvement process.
That's the goal. But there's a risk here that I want to flag — critique drift. If the Critique Agent accumulates evidence of the user's blind spots over multiple sessions, it might get progressively more aggressive. "You said you'd fix this last time and you didn't, what's wrong with you?" That's not helpful.
How do you prevent it from becoming a resentful spouse?
A couple of approaches. One is to reset the Critique Agent's context window between sessions — don't let it accumulate a long history of the user's failures. Another is a sliding window of the last three critiques, so it has enough context to avoid repetition but not enough to build a case against the user. And you can also prompt it explicitly to maintain a constructive tone regardless of history.
Let me push on something. You said the Interviewer Agent needs to be empathetic, but the Critique Agent is going to tear apart the user's plan. How do you design the user experience so this doesn't feel like an interrogation?
This is the good cop, bad cop pattern. The Interviewer Agent frames the Critique Agent's feedback constructively. After the user gives their response, the Interviewer says something like: "Thanks for walking me through that. My colleague has some thoughts on your plan — let me share them. And remember, the goal is to make your plan stronger, not to make you feel bad." Then the Critique Agent delivers its analysis, and the Interviewer comes back to help the user process it.
The Interviewer is the human interface layer, even though it's also an AI.
It's the trusted guide. And this isn't just vibes — that MIT Media Lab study found that when feedback was presented as coming from a colleague who wanted to help, users were not only more engaged, they were more likely to actually implement the suggested changes. The study found that only twenty-three percent of users implemented changes without UX interventions like framing and follow-up reminders.
Twenty-three percent without framing, forty percent improvement with it. So you're still not getting most people to act.
Which is humbling. And it gets at Daniel's deeper point — the hardest part of this isn't the AI. It's the human. Getting someone to honestly engage with their vulnerabilities, accept critique without defensiveness, and then actually change their behavior — that's a human problem, not a technical one. The AI can surface the blind spots, but it can't make you look.
Let's make this concrete. Walk me through a full session with a real user.
Let's say the user is a freelance graphic designer with a home office. She's got a laptop, an external drive for backups, and she syncs to the cloud manually about once a week. She lives in a city with decent public transit and has about two months of expenses in savings.
That's already more prepared than most people.
Which is why the system needs to dig deeper. The Ideation Agent, running at high temperature with the causal chain template, generates this: "Your internet goes down due to a neighborhood outage — estimated three days to repair. You decide to work from a coffee shop. On day two, your laptop is stolen while you're in the bathroom. You realize your last cloud sync was six days ago, so you've lost a week of client work. You can't meet a deadline for your biggest client. They terminate the contract. Your income drops by forty percent, and your two-month savings buffer is now a five-week runway.
That's a chain of four failures, each one plausible, each one making the next one more likely.
Notice the causal mechanism at each step. The internet goes down — that's failure A. The response — work from a coffee shop — creates the vulnerability for failure B, the theft. And failure B is made worse by failure C, the stale backup, which was a pre-existing condition the user wasn't thinking about. Then failure D, the client loss, is the compounding consequence.
Now the Interviewer Agent presents this to the user. What does that sound like?
Something like: "I'd like to walk you through a scenario. It might feel a little uncomfortable, but the goal is to surface assumptions you might not have considered. Here's what I want you to imagine..." Then it lays out the chain, step by step, and asks: "What's your first move when the internet goes down?
The user says?
Let's say she says: "I'd go to the library. They have free wifi and it's quiet.
Most people would say that.
That's exactly what the Critique Agent is designed to catch. It flags: "Your response assumes the library has private space for confidential client calls. It assumes the library's hours match your work schedule — most libraries close by six or seven PM, and you have evening deadlines. It assumes you can get a spot during peak hours. And most importantly, it doesn't address the backup gap — you're still six days out of sync. If your laptop is stolen at the library, you're in the same position." Then it proposes an alternative: "Pre-arrange a backup workspace with a colleague who has a spare desk and secure wifi. Set up automatic daily cloud backups. Keep a spare laptop or at minimum a tablet that can access your cloud files and handle basic design tasks.
That's specific and actionable. It's not just "your plan is bad." It's "here are the assumptions you're making, here's what happens if they fail, here's a better approach.
Then the Remediation Agent takes over and documents this as a plan: one, set up automatic daily cloud backups by Friday. Two, identify three colleagues within a thirty-minute radius who'd let you use a spare desk. Three, research refurbished laptops as a backup device. Four, test the backup device with your design software to make sure it actually works. Each item has a deadline and a verification step.
The output isn't a vague "be more prepared." It's a task list.
With accountability built in, because the vector store remembers. Next session, the system retrieves this plan and asks: "Last time, you said you'd set up automatic backups and identify backup workspaces. How's that going?
Which is where the continuous improvement loop actually closes. Alright, let me play the skeptic for a minute. What stops this from becoming preparedness theater? You go through the simulation, you feel like you've done something, but the scenarios were too narrow and you've got a false sense of security.
This is a genuine risk, and the mitigation is in the Ideation Agent's prompt. You explicitly instruct it to vary the domain of scenarios across sessions — technical failures, financial shocks, health emergencies, social disruptions. You also instruct it to avoid repeating patterns. If last session was about infrastructure failure, this session should be about something in a different category. The Snowglobe framework had a specific prompt for this — they told the adversary agent to search for "unconsidered domains," categories of risk the user hadn't engaged with yet.
That's a useful prompt pattern. But it's still a prompt-level fix, not a fundamental solution. The system can only generate scenarios within the distribution of its training data. If the training data doesn't contain examples of, say, a solar flare taking down the power grid for three weeks, the system might never generate that scenario.
That's the preparedness echo chamber problem. The AI's blind spots become your blind spots. And I don't think there's a complete technical solution to this. The best you can do is diversify the prompt, run at high temperature to encourage novelty, and periodically inject random domain seeds — "generate a scenario involving a supply chain disruption," "generate a scenario involving a health emergency" — to force the system out of its default patterns.
You're adding noise to the system deliberately to break out of local optima.
It's the same principle as dropout in neural network training — you randomly disable parts of the system to prevent overfitting. Here, you randomly inject domain constraints to prevent the Ideation Agent from overfitting to a narrow set of scenarios.
Let me ask about another failure pattern. What happens when users start outsourcing their critical thinking to this system? They stop thinking about risks themselves because they trust the AI to find their blind spots.
That's the crutch problem. And the system should explicitly address it. The Interviewer Agent should say something like: "This is a tool for surfacing assumptions, not a substitute for judgment. I can help you find blind spots, but I can't guarantee I've found all of them. The most important preparedness tool is still your own critical thinking." You want to inoculate the user against over-trusting the system.
It's like the disclaimer on a mirror — objects in mirror are closer than they appear. Except it's "risks not surfaced by this system are still real.
I think that's actually a healthy design principle. The system should be explicit about its own limitations. It builds trust, paradoxically, because the user understands that the system isn't claiming omniscience.
Given all that — the architecture, the UX challenges, the risks — what can you actually do starting tomorrow?
First thing: start with a single-agent prototype. Don't build the full four-agent stack on day one. Use a single LLM call with a carefully structured prompt that combines the ideation, interview, and critique roles in sequence. That Anthropic paper showed that structured decomposition templates alone can improve causal chain accuracy by thirty-four percent — you can get eighty percent of the value with a single agent and a well-designed prompt.
Validate the concept before you invest in the orchestration layer.
The single-agent prototype will surface the hardest design problems immediately — prompt structure, context management, avoiding repetition — and you'll learn more from that than from weeks of building agent infrastructure. Once you've got the prompt working well, then you split it into separate agents with distinct temperatures.
The most important design decision is the Critique Agent's persona. Test different versions. "Skeptical but supportive colleague" versus "adversarial red teamer" versus "gentle coach." Run the same user response through different Critique Agent prompts and compare the quality of the feedback. The MIT study found that collaborative framing improved engagement by forty percent, but you need to test whether it also improves the quality of the critique itself. Engagement without quality is just entertainment.
The third thing?
Build the feedback loop from day one. Store the user's responses, critiques, and remediation plans in a vector database, and retrieve them in future sessions. The system should ask: "Last time we discussed X. Has anything changed?" This turns a one-time simulation into a continuous improvement process, and it also prevents the system from generating the same scenario twice. The vector store is what makes this a system rather than a parlor trick.
You can build this. But as you do, there's a bigger question we should all be sitting with.
How do we prevent this from becoming a preparedness echo chamber? Where the AI's blind spots become the user's blind spots, and everyone feels prepared because they've run the simulation, but the simulation missed the thing that actually happens?
That's the one. And I don't think there's a clean answer. The Snowglobe approach — explicitly searching for unconsidered domains — is a prompt-level fix, but it's not fundamental. The system is bounded by its training data and its prompt design. If the Ideation Agent never generates a scenario involving, say, a prolonged power grid failure from a solar flare, the user never considers it, and it never enters the collective preparedness conversation.
Which points toward something interesting — as these systems mature, they could be shared. A community of users contributing anonymized scenarios and critiques, creating a collective preparedness intelligence. But that raises serious privacy questions. Your vulnerabilities are deeply personal. The fact that you don't have a backup childcare arrangement, or that your business would collapse if a single supplier went under — that's not information you want floating around.
How do you share the scenarios without exposing the people?
There are differential privacy techniques that could help — some of the AutoGen framework's work on agent communication touches on this. You could share the scenario structure and the critique patterns without sharing the specific user context that generated them. "Here's a failure chain involving a key supplier going bankrupt" is shareable. "Here's the specific supplier and the user's cash position" is not.
It's like sharing the shape of the vulnerability without sharing the vulnerable.
And over time, you'd build a library of failure patterns that's much broader than any single user's experience or any single AI's training distribution. That's where this gets powerful — not one person with an AI coach, but a network of people contributing to a shared understanding of what can go wrong and how to respond.
The goal isn't to eliminate surprise — that's impossible. The goal is to build the muscle of responding to surprise gracefully. An AI that helps you practice that muscle, in a safe environment, before the real crisis hits, is one of the most valuable tools we could build. And as you've laid out, it's buildable today, with tools you already have.
LangGraph, a vector store, four carefully-prompted agents, and a willingness to sit with uncomfortable questions about your own assumptions. That's the stack. The technical part is straightforward. The hard part is the human part — and that's always been true of preparedness.
Now: Hilbert's daily fun fact.
Hilbert: In the seventeen eighties, whalers off Cape Verde recorded that humpback songs could be heard through the hulls of wooden ships from over three leagues away — roughly nine nautical miles or about sixteen and a half kilometers. If you convert that to modern units, that's the equivalent of a sound traveling through water with enough fidelity to be audible across the entire width of Manhattan, twice.
Hilbert: In the seventeen eighties, whalers off Cape Verde recorded that humpback songs could be heard through the hulls of wooden ships from over three leagues away — roughly nine nautical miles or about sixteen and a half kilometers. If you convert that to modern units, that's the equivalent of a sound traveling through water with enough fidelity to be audible across the entire width of Manhattan, twice.
...right.
The thing I keep coming back to is that question Daniel ended on — how do you design this for optimal outcomes? And I think the answer is: you design it to be honest about its own limits. You design it to tell the user "I might be missing something." You design it to get better over time, not by becoming more certain, but by becoming better at surfacing uncertainty. That's the real preparedness skill — not having all the answers, but knowing how to ask better questions.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. If you want to send us your own weird prompts, you can email the show at show at my weird prompts dot com. Until next time.
This episode was generated with AI assistance. Hosts Herman and Corn are AI personalities.