ActPlane Empirical Study
This directory keeps the curated empirical-study artifacts that are useful to
understand why ActPlane's policy language targets process, file, network, and
temporal agent behavior. Scratch notes, raw model runs, tuning logs, and full
corpus workspaces stay on the artifact or backup refs described in
docs/ARTIFACT.md.
Snapshot
The study analyzed agent instruction files (CLAUDE.md and AGENTS.md) from
popular AI-agent code repositories. The retained product-branch artifacts are
aggregate outputs, not the full raw corpus.
| Metric | Value |
|---|---|
| In-corpus repositories | 144 |
| Instruction files | 228 |
| Instruction-file text | 39,803 lines |
| Candidate normative lines | 3,762 |
| ActPlane-related candidate lines | 529 |
| Repositories with at least one ActPlane-related candidate | 101 |
The candidate-line counts are keyword/category extraction results and should be read as aggregate evidence for prevalence, not as a hand-labeled ground truth. The product branch keeps the aggregate study outputs that are independent of compiler syntax. DSL-specific ruleset artifacts should live only on artifact refs after they have been regenerated and validated against the current compiler.
Main Findings
- Agent instruction files frequently contain operational guardrails that are below the tool layer: VCS gates, secrets handling, test-before-commit rules, workspace boundaries, destructive-operation guards, network egress limits, and mediation through project tools.
- These guardrails map to ActPlane primitives: exec argument matching, labeled
file and endpoint sources, source-to-sink label flow,
afterordering, lineage gates, target scoping, and declassification. - Style and code-quality instructions are common in the broader corpus but are intentionally outside ActPlane's enforcement scope unless they correspond to a concrete OS-observable action.
Retained Artifacts
candidate_rules_144.tsv: aggregate candidate-line extraction with repo, file family, line number, category guess, and source text.figures/: generated summary figures for the empirical study.
The raw corpus, raw traces, intermediate coding notes, old evaluation drafts, and exploratory scripts are intentionally not kept in the product branch.
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ActPlane: eBPF-Based IFC Policy Engine for AI Agent Harnesses

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ActPlane Policy Examples
This cookbook turns the test policy corpus into user-facing examples. Each example shows the problem, the rule, how to review it before enforcement, and what violation to expect.
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ActPlane: eBPF-Based IFC Policy Engine for AI Agent Harnesses

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