Decoding YC’s Startup Requests

Hi I’m Angela 🧸A product growth marketer who exists in the space between caffeine highs and retention lows.

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Quoting Melanie Goodman ’s comment under this article, I think it’s a perfect opening:

The real magic here is that YC’s Requests aren’t just them saying “we like X”, they’re publishing a structured demand map for systemic inefficiencies. It’s a form of market signalling theory in action - they’re pointing to areas where capital can create network effects faster than incumbents can react. According to CB Insights, 70% of startups fail because they misread market demand or chase low-barrier spaces (cbinsights.com/research…). That’s why YC’s focus on “institutional design” — standardisation, trust protocols, monitoring frameworks — matters so much. These are not MVP-and-hope bets, they’re long-term moats with compounding returns.Which of YC’s highlighted bottlenecks do you think is most underpriced by the market right now?

YC’s latest Requests for Startups do more than just list what’s “hot” right now. They trace where money is moving, revealing deeper cracks and opportunities in how the economy actually works. Investors don’t chase every shiny object; they hunt persistent imbalances - those pain points where supply, demand, and incentives don’t line up. For founders, the challenge isn’t copying trends but understanding why these patterns exist and how to build something that lasts beyond the hype. This piece pulls back the curtain on YC’s signals, treating them as clues to a larger system at work.

Infrastructure for Multi-Agent Systems:

The Real Bottleneck YC Is Betting On

The following analysis of

* how to write effective agent and subagent prompts

* how to handle untrusted context

* how to monitor and debug these agents

is based on excerpts from YC’s Requests for Startups Fall 2025.

* How to write effective agent and subagent promptsPrompts serve as the “instruction language” for multi-agent systems, shaping the quality and direction of agent behaviors. The challenges include:

* Diverse agent tasks mean a single template cannot cover every scenario

* Subagents need to inherit the parent agent’s intent, but information often gets distorted in transmission

* Overly long prompts increase cost and latency

Entry points and opportunities:Build a modular, composable prompt template library that provides standardized samples for different tasks and levels. This reduces communication costs and errors through standard protocols - a classic institutional design problem.

Continuous iteration based on user feedback creates a closed-loop optimization system.

Recent startups like PromptLayer offer tools to track, version, and optimize prompt templates, showing early market demand. Additionally, some teams use AI to auto-generate prompt variations and run A/B tests, reducing manual overhead and increasing scale.

* How to handle untrusted contextIn multi-agent systems, information passed from various subagents may contain errors or malicious data, causing systemic bias or collapse.

Entry points and opportunities:Designing multi-layered trust and verification mechanisms is critical. This includes:

* Implementing authorization and identity verification between agents to prevent malicious subagents from infiltrating

* Using redundancy checks, where multiple agents cross-validate information to improve accuracy

* Establishing anomaly detection and rollback policies that trigger human or AI review when suspicious results arise

This trust architecture resembles risk control systems in finance or law - hard to replicate and with a high barrier to entry. Early investment here can create long-lasting competitive moats.