i think there's probably a $100b prompt waiting out there that puts the models 0.5 generations ahead
AGENT ARCHITECTURE OPTIMIZATION
Systematically test multiple scaffolds, models, and architectures in parallel. Find the most accurate and cost-efficient agent configuration for your use case.
10x
Cost reduction
N
Scaffolds
1
Optimal
THE PROBLEM
Practitioners face significant challenges navigating the complexity of AI agent architectures.
Too many AI models, tools, and techniques. The ecosystem is fundamentally disconnected, making it impossible to track best practices.
Most agents today are just an LLM paired with tools. This simple approach fails to leverage advanced architectural patterns.
Teams skip architecture optimization. As one YC founder noted: "Most people don't really do this... It's just agents + tools."
THE SOLUTION
SquareDiff transforms agent development from an intuitive, ad-hoc process into a systematic, empirical science. Think of it as Replit for A/B testing vastly different agent scaffolds.
"There's probably a $100B prompt out there that puts models 0.5 generations ahead."
— The right architecture can unlock equivalent performance gains.
Provide a high-level, long-horizon task or query that your agent needs to complete.
SquareDiff automatically creates multiple, varied agent scaffolds from a library of established patterns.
Each scaffold is paired with models and tools, all attempting to complete your task concurrently.
Outputs are analyzed to identify the most performant architecture based on accuracy, cost, and speed.
HOW IT WORKS
One prompt, many architectures, one optimal output.
THE DESIGN SPACE
SquareDiff explores a comprehensive library of architectural patterns across multiple dimensions.
ReAct
Think-act loop for stepwise reasoning
Plan-then-Execute
Structured multi-step planning
Reflect / Critique
Self-review for higher reliability
Verifier Loop
Strict constraint validation
Supervisor-Worker
Hierarchical delegation
Role-based Specialists
Committee of experts
Debate / Deliberation
Adversarial collaboration
Router / Dispatcher
Fast path selection
Dynamic Tool Discovery
On-demand tool retrieval
Programmatic Calling
Code-orchestrated tools
Ensembling
Parallel merge for robustness
Mixture-of-Agents
Layered refinement
INDUSTRY VALIDATION
Leading practitioners and researchers confirm: agent architecture optimization is the next frontier.
We're building the future of systematic agent architecture testing. Let's talk about how SquareDiff can help your team.
Get in Touchi think there's probably a $100b prompt waiting out there that puts the models 0.5 generations ahead
two big themes of AI in 2026 will be enterprise agent adoption and scientific acceleration
The harnesses are still not optimally utilizing the models Everyone rightfully loves claude code right now, but it is clear to me it is underutilizing the capability of opus. The lowest hanging fruit is /init in existing code bases. It does a good job of building it's Show more
