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•January 8, 2026

Ingestion gates and human-first approval for agent-generated code

Quick Overview

As AI coding assistants become more prevalent, there's a growing need for explicit human-first approval and clearer standards for agent-generated code to prevent insidious failures and ensure secure execution.

  • Security Concern: Agent-generated code is not trustworthy by default, necessitating human-first approval and "ingestion gates" before execution to prevent surprises and maintain intentional control.
  • Standardization Need: The `AGENTS.md` specification is being updated (v1.1) to clarify underspecified edge cases, ensuring consistent interpretation of agent behavior across different tools.
  • Behavior vs. Capability: `AGENTS.md` will explicitly focus on agent behavior (rules, constraints) while `SKILL.md` (aka "Claude Skills") addresses agent capabilities (tools, domains), positioning them as complementary.
  • Workflow Automation: Tools like the "Agent Skills Generator" aim to simplify the creation of custom instructions for AI coding assistants, enabling users to teach them specific workflows in plain English.
  • Implicit Semantics: The `AGENTS.md` v1.1 proposal formalizes filesystem semantics for agent instructions, including jurisdiction, accumulation, precedence, and implicit inheritance, to align tool implementations.

Key Points

Background Context: The Evolving Landscape of Agent-Generated Code

  • The increasing adoption of AI coding assistants and agentic tools, such as GitHub Copilot and Claude, is leading to more agent-generated or modified executable code ('OpenAI', 'r/SideProject').
  • There is a growing recognition that current specifications for agent behavior, like `AGENTS.md`, are underspecified for important edge cases, leading to inconsistent interpretations across tools ('OpenAI').
  • This evolving landscape introduces new challenges, particularly around security and the control of automated processes that can generate executable code ('cybersecurity').

Proposed Solution: Human-First Ingestion Gates for Security

  • Ingestion gates as hard security boundaries: Generated code should not execute by default; execution requires deliberate authorization, similar to a 'decontamination chamber' ('cybersecurity').
  • Pre-execution control: The focus should be on the 'moment before execution' when generated code enters the system, rather than relying solely on post-execution monitoring ('cybersecurity').
  • Assumptions for secure design: Generated code is not trustworthy by default, sandboxing limits damage but doesn't prevent issues, and automation without gates erodes intentional control ('cybersecurity').

Clarifying Agent Behavior Guidance: AGENTS.md v1.1 Proposal

  • Addressing underspecification: The `AGENTS.md` v1.1 proposal aims to clarify semantics for agentic behavior guidance without reinventing the format, to address issues like conflicting files, instruction loading, and context compaction ('OpenAI').
  • Distinction between behavior and capability: `AGENTS.md` focuses on 'how should the agent behave?' (rules, constraints) while `SKILL.md` focuses on 'what can this agent do?' (capabilities, tools), positioning them as complementary ('OpenAI').
  • Formalizing implicit semantics: The proposal documents concepts like jurisdiction, accumulation, precedence, and implicit inheritance for `AGENTS.md` files, ensuring a shared mental model for users and tool developers ('OpenAI').

Implications for Trust, Control, and Development

  • Erosion of intentional control: Automation without deliberate gates can quietly override human control, leading to a 'risk multiplier over time' due to unclear intent and fuzzy provenance of generated code ('cybersecurity').
  • Need for shared mental model: Users need confidence that different agentic tools supporting `AGENTS.md` will have a consistent understanding of behavioral expectations, which is currently lacking due to underspecified guidelines ('OpenAI').
  • Developer tooling for skills: Tools like the 'Agent Skills Generator' help users define custom instructions for AI coding assistants, suggesting a growing need for clear, well-defined specifications to teach agents specific patterns and workflows ('r/SideProject').

Outline

No outline available

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