A team just got into YCombinator by building something that sounds almost too ambitious. Their platform takes a business idea and runs the whole thing. Storefront, product sourcing from AliExpress and Alibaba, ads on Google and Facebook and Instagram. Fully autonomous. Continuous operation.
Here’s the twist: the part that actually made it work wasn’t the multi-agent architecture. It was a single context decision.
Early on, individual agents were producing solid outputs that contradicted each other. The fix wasn’t more complex prompting. It was passing the full intake context object to every downstream agent, untouched. Not a summary. The full thing. That one change fixed more coherence problems than everything else they tried combined.
Here’s what the pipeline looks like:
🔹 Intake interview. A conversational prompt that feels natural to the user while extracting structured data underneath. Getting this right took more iterations than anything else in the system. Open questions produced interesting ideas with no structure. Structured questions felt like a form and killed completion rates. The hybrid is the genuinely hard part.
🔹 Full context propagation. One structured object flows to every agent downstream. No compression, no summarization between steps. This is the architectural decision that held everything together.
🔹 Judgment prompting for operations. Instead of prompting agents to execute directly, they ask agents to reason about what a skilled human would do in the situation first, then act. Measurably better decisions in changing conditions.
🔹 Continuous autonomous operation. Ads run, copy updates, products adjust. No human needed after intake.
Pro tip: The full context rule applies well beyond multi-agent systems. Any time you summarize or compress context between pipeline steps, you introduce silent information loss. That loss shows up later as incoherence, and it’s genuinely hard to trace back to the source.
The unsolved problem they’re still wrestling with: getting agents to recognize when they’re outside their competence and stop, instead of confidently executing on a wrong call. Partial solutions exist. No complete answer yet.
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Frequently Asked Questions
Q: How do you prevent users from dropping off during conversational intake?
Use a staged interview instead of one magic prompt. Start with rapport-building questions, then run a hidden pass to extract rough structure and flag low-confidence fields. Users only see 2, 3 targeted clarifiers instead of a full form, way less drop-off.
Q: Why pass full context instead of a summary to downstream agents?
Summaries feel safe until edge cases emerge and they silently drop the one detail an agent needed. Full context ensures downstream agents have everything to make coherent decisions instead of confidently executing on incomplete information.
Q: How do you stop agents from confidently executing outside their competence?
Separate “planner” and “executor” roles with explicit refusal criteria, the executor must cite a required signal, tool output, or invariant before acting. It adds friction but catches overconfident calls. Note: this is a partial solution; the complete answer is still being figured out.
Q: What’s the best way to turn conversational input into structured output?
Don’t jump straight to JSON. Have the model generate candidate structure + rationale first, then use a validator model to snap it to strict schema, fill defaults, or flag gaps for follow-up questions. Two-pass approaches reduce errors and show your reasoning.
Q: Which monitoring tools actually catch intake failures in production?
Off-the-shelf tools often miss edge-case threads where your system breaks. Pulse for Reddit plus custom dashboards worked better, homegrown monitoring surfaces failure patterns specific to your system instead of generic trends.
Built a production multi agent system that runs businesses autonomously. Here’s every prompt architecture decision that actually mattered and what we learned the hard way.
by u/IAmDreTheKid in PromptEngineering