#49: Forget LLMs? NVIDIA Says Small Models Will Power AI Agents

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Why Small Language Models Could Power the Next Wave of AI Agents

NVIDIA just dropped a research paper with a bold claim: small language models (SLMs), not giant LLMs, are the real future of agentic AI. At first glance, it sounds counterintuitive - after all, LLMs like GPT-5 and Claude dominate headlines. But NVIDIA’s argument is refreshingly pragmatic.

Here’s the gist:

• Most agents don’t need “infinite wisdom.” The majority of agent tasks - like calling APIs, formatting structured data, or generating boilerplate code - are narrow, repetitive, and rule-bound. You don’t need a 175B-parameter model to schedule a meeting or process a JSON output.

• SLMs are getting seriously capable. Models like Microsoft’s Phi-3 (7B), NVIDIA’s own Nemotron-H (2–9B), and Salesforce’s xLAM-2 (8B) are matching or beating older 30B–70B models at reasoning, tool calling, and code generation. That means small is no longer “weak.”

• Economics matter. Serving an SLM is 10–30× cheaper than an LLM. They run faster, burn less energy, and can even live on consumer GPUs (hello, on-device agents). For businesses, that means scalability without bankrupting on inference costs.

• Modularity beats monoliths. Instead of one all-knowing LLM, NVIDIA suggests a “Lego-style” approach: combine a handful of small, specialized agents for specific jobs, and bring in a big model only when truly needed.

One striking takeaway: NVIDIA estimates that in open-source agent frameworks like MetaGPT or Cradle, 40–70% of LLM calls could already be swapped out for SLMs without losing performance.

Why this matters:

• If you’re building agents, SLMs mean lower costs, faster iteration, and easier fine-tuning. You can adapt overnight instead of waiting weeks for an LLM fine-tune.

• If you’re using agents, it points toward a future where AI tools are cheaper, more private (local execution), and more abundant - not bottlenecked by a handful of massive cloud APIs.

• If you’re investing in the AI space, this shift could reshape infrastructure bets - from centralized LLM superclouds toward distributed, specialized SLM ecosystems.

Agents.md — A README for AI Agents

OpenAI and partners just introduced Agents.md, a simple but powerful standard: a Markdown file that lives alongside your code to guide AI agents on how to interact with your project. Think of it as a README, but for ai agents.

Instead of bloating human-facing docs with build commands, test scripts, or linting rules, you put those details into AGENTS.md. Now, tools like OpenAI Codex, Cursor, Google’s Gemini CLI, and Sourcegraph’s Amp can automatically parse it and “know” how to behave inside your repo.

Why this matters

1. Standardization over fragmentation
   Until now, every AI coding agent had its own configs and quirks. Agents.md offers a single, shared format - so your project works across ecosystems instead of locking into one tool.

2. Smarter automation, fewer mistakes
   Agents can run builds, tests, and PR checks the way your team does them - because you’ve spelled it out once in agents.md. This reduces the risk of an agent “hallucinating” commands or ignoring team conventions.

3. Modularity for complex codebases
   You can drop multiple agents.md files into different subfolders of a repo. That means a monorepo with frontend, backend, and infra can give tailored instructions to agents at each level.

The bigger picture

This isn’t just an OpenAI experiment. Factory.ai, Sourcegraph, Google, and OpenAI are all backing the spec. If adoption spreads, agents.md could become a de facto interface layer between humans and AI agents, much like README.md or package.json became foundational in their eras.

It’s also part of a larger trend: moving from “generalist AI agents that figure it out” to “AI agents that reliably follow project-specific rules.” In other words, agents that behave like trained teammates, not freelancers guessing at your process.

What to watch

• Adoption curve: If GitHub, GitLab, or major frameworks start auto-scaffolding agents.md, it could spread fast.

• Ecosystem lock-in vs. open standard: Right now it’s open, but if one vendor twists the spec, we could see fragmentation creep back in.

• Beyond code repos: Imagine a agents.md-style file guiding business agents - customer support, sales ops, finance automation—on how to safely interact with company workflows.

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