As enterprises increasingly adopt AI agents to automate complex workflows, enhance customer experiences, and optimize operations, the way we design and scaffold these systems is undergoing a profound transformation. Drawing on the “bitter lesson” from AI research, Rahul Sengottuvelu advocates leveraging massive computation and general-purpose learning over rigid, handcrafted heuristics. This article explores his insights, aligns them with the no-code agentic AI philosophy of lowtouch.ai, and provides strategies for building scalable, robust AI agents.

The “bitter lesson,” as Rich Sutton observed, shows that systems using scalable computation and general methods outperform those relying on manual engineering. Historical breakthroughs—from chess engines to computer vision—underscore this trend. Early handcrafted heuristics yield short-term gains, but massive compute and learning-based approaches scale indefinitely.

At Ramp, the switching report agent processes arbitrary CSV schemas from card providers. Three scaffolding approaches illustrate the bitter lesson:

Approach 1: Deterministic Code

• Hand-written parsers for each provider (50+).
• Reliable but labor-intensive and brittle.

Approach 2: Hybrid Constrained AI

• LLM classifies columns; scripts handle parsing.
• More flexible but still complex to maintain.

Approach 3: Fully AI-Driven

• Feed CSV to LLM with code interpreter (pandas, Rust).
• Run 50 parallel jobs for robustness.
• Minimal engineering, maximal compute—scales with model improvements.

These approaches map to:

• Classical Land: Pure deterministic code—rigid and manual.
• Constrained Agent: Mixed scripts + AI calls—improved flexibility but complex.
• AI-Driven: LLM orchestration with on-demand classical compute—highly adaptable.

Lowtouch.ai’s no-code platform embodies AI-driven scaffolding: agents leverage ReAct and CodeAct to invoke tools dynamically, relying on LLM “fuzzy compute” for most logic.

Sengottuvelu demonstrates an email client where the LLM replaces traditional backend logic. The model renders UIs in markdown, handles user actions by generating code or API calls, and drives the entire application—entirely via compute.

Lowtouch.ai parallels this vision: its appliance-based platform runs LLMs privately, serving as the backend for agentic workflows. A conversational UI, OpenAI-compatible API, and vector database enable truly dynamic, compute-driven applications.

• Maximize Fuzzy Compute: Let LLMs drive reasoning; use classical tools only when needed.
• Start with Capable Models: Prototype on top LLMs, then optimize with smaller ones. Lowtouch.ai supports private Nemotron and Llama models.
• Leverage No-Code: Empower non-technical teams—build agents without code via lowtouch.ai’s platform.
• Embrace Parallelism: Run tasks in parallel to ensure robustness over diverse inputs.
• Implement Guardrails: Use relevance classifiers, PII filters, and human-in-the-loop for safety.
• Plan for HITL: Escalate high-risk actions to humans via OTP, collaboration tools, or links.
• Use Vector DB for Context: Enable RAG and semantic search for dynamic knowledge retrieval.
• Iterate Incrementally: Start with single agents, evolve to multi-agent patterns as complexity grows.

• No-Code Simplicity: Drag-and-drop agent creation eliminates engineer time.
• Privacy-First Design: Runs within customer infrastructure, with air-gapped options.
• Scalable Architecture: Private and external LLM support, vector DB for context.
• Rapid Impact: Deploy in weeks, not months, with measurable ROI.

Embracing the bitter lesson means shifting from handcrafted heuristics to compute-driven AI architectures. By maximizing fuzzy compute, leveraging no-code platforms, and implementing robust guardrails, enterprises can build AI agents that scale, adapt, and evolve with model improvements. lowtouch.ai empowers this transformation—delivering agentic automation that is simple, private, and future-proof.

For CISOs, CIOs, and CTOs ready to harness scalable AI agents, visit lowtouch.ai or email info@lowtouch.ai.