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February 17, 2026

Agent Lightning ↗

project

Microsoft Research · 2025

  • Decouples agent optimization from agent execution via a sidecar that collects traces non-intrusively — the same separation of concerns that Unix process boundaries already enforce between a program and its observer
  • The sidecar pattern is tee for training data: watch what the agent does, capture the interaction traces, optimize separately — no modification to the agent’s own code or workflow
  • Proves that optimization is orthogonal to orchestration; if your agent is a shell loop, the traces are already in your filesystem (stdout logs, exit codes, tool call records) and the training pipeline is just another downstream consumer

llm-functions ↗

project

sigoden · 2024

  • Defines LLM tools as plain Bash functions with structured comments — the tool schema is generated automatically from the script itself, no SDK or serialization layer needed
  • Agents are composed from tools + prompts + documents, assembled at the filesystem level; adding a capability means dropping a shell script into a directory
  • Proves that function calling does not require a framework: a shell function, a naming convention, and a comment block are sufficient for an LLM to discover and invoke a tool
February 16, 2026

agent-browser ↗

project

GitHub / Vercel · 2026

  • Browser automation CLI that reduces context usage by 93% through a “snapshot + refs” system — elements get short labels (@e1, @e2) instead of dumping the full accessibility tree into the LLM
  • Three-layer architecture (Rust CLI → Node.js daemon → Playwright) that looks like any other Unix tool from the agent’s perspective: commands in, structured output out
  • Same thesis as Playwright CLI but pushed further — the agent controls a browser through shell commands, reinforcing that tool use is just command execution with minimal data passing

Playwright CLI ↗

project

GitHub / Microsoft · 2025

  • Browser automation as a CLI instead of MCP — agents discover commands from help output rather than tool schemas, proving that shell conventions are sufficient for tool integration
  • Deliberately “token-efficient” by not forcing page data into the LLM context, which is the Unix philosophy applied to agents: do one thing, pass minimal data between steps
  • Validates the thesis that agent tool use reduces to command execution — browser automation is just another program the model shells out to