AI agents · reliability machinery · security boundaries · strange software with explicit authority

I build systems that let agents act without making their authority invisible: local-first collaboration, protocol enforcement, production-failure feedback loops, adversarial validation, and evaluation infrastructure. The interface changes. The constraint does not: the machine should remain inspectable when something goes wrong.

TYPE A2A INFRASTRUCTURE　 STATUS CP0 COMPLETE + FROZEN
Customer-local enforcement seam for external A2A agents. It proxies the frozen A2A 1.0 profile while separating external caller credentials from customer-local upstream authority.
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TYPE SECURITY VALIDATION　 STATUS PRIVATE VALIDATION MVP
Passive inspection of newly created public GitHub repositories with bounded acquisition, hostile-input handling, sanitized evidence, and a deliberate zero-retention boundary for source bytes.
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TYPE LOCAL-FIRST OPERATOR SYSTEM　 STATUS PHASE Z0
Pre-billing revenue assurance for freight brokerages: an advisory, read-only workflow that finds carrier-side accessorial costs missing or underrepresented in customer billing.
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TYPE AI RELIABILITY　 INVENTORY 100 EXECUTABLE LOOPS
Automated tests for LLM systems spanning evaluation, guardrails, red-teaming, chaos engineering, stress testing, and standards-mapped reporting.
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TYPE FAILURE FEEDBACK LOOP　 ROUTE PRODUCTION → REGRESSION
Captures failed LLM interactions, retrieves their execution traces, classifies failures, generates DeepEval regression tests, and opens pull requests.
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TYPE MULTI-AGENT ORCHESTRATION　 MODE REAL-TIME SSE
A LangGraph and FastAPI system that decomposes tasks through retrieval, critique, and synthesis with structured agents, observability, and evaluation.
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SYSTEM ACTIVITY // 2025—2026
CLASSIFICATION: SYNTHETIC RESEARCH-INTENSITY ATLAS

These plates are generative profile artwork: fictional maps of experiments, build intensity, silence, and agent propagation. They are not GitHub contribution history and do not claim activity on any date.

_{Each plate is deterministic, config-driven, and rendered by a zero-dependency Python generator. Seven pattern fields are available: random, clustered, agent swarm, cathedral, brutalist, signal noise, and architectural grid. INSPECT THE GENERATOR →}

Custodian A‑17 appears wherever a boundary needs watching. The smaller agents retrieve, dispute, observe, and record. None of them are trusted merely because they are inside the building.

The archive is allowed to grow only when the work is real.

/lab/YYYY/MM/DD/       experiment notes, decisions, measured outcomes
/evals/coding-agents/  benchmark inputs, versions, results, interpretation
/evals/retrieval/      retrieval evaluations and regression evidence
/evals/planning/       planning-task suites and score changes
/evals/tool-use/       tool contracts, failures, and recovery behavior

Generated records should name their inputs, tool or model versions, procedure, result, and limitations. Automation may commit a meaningful changed artifact. It must never create empty commits, rewrite dates, or manufacture activity.

If you are working on agent protocols, evaluation machinery, local-first systems, hostile-input handling, or software that turns failures into durable evidence, the door is not locked.

github.com/0xnotdev　/　open a transmission