AI Systems & Agent Reliability Engineer · Founder, RUMBO IA

Public surfaces: RUMBO IA · 90-second agent state-drift video · @RumboAGI on X

I build agentic systems where intent, authority, execution, observed effect, and promotion are separate, testable states.

INTENT → AUTHORITY → PREFLIGHT → EXECUTION → READBACK → FALSIFICATION → CLOSURE

My focus is failure-boundary engineering: async resource ownership, cancellation safety, fail-closed execution, tool and permission routing, deterministic evaluation, canonical-state recovery, and verifiable effects.

An installable, runtime-dependency-free Python reference implementation for fail-closed agent execution with exact-state binding, deterministic revalidation, effect readback, replay prevention, and hash-bound receipts.

The test suite covers stale-state rejection, duplicate intent rejection, action allowlisting, secret-like payload rejection, readback mismatch, receipt-chain verification, tamper detection, and replay prevention.

Developer quickstart: 5-minute Reliability Quickstart walks through INTENT → AUTHORITY → MATERIALIZATION → READBACK → RECEIPT → VERIFICATION.

Technical deep dive: An accepted agent action is not necessarily executable explains DECISION_ACCEPTED != EXECUTION_SAFE and the exact pre-effect state-drift guard.

90-second video: An Accepted Agent Action Is Not Necessarily Executable demonstrates the same fail-closed state-drift boundary and the negative observation blocked_target_mutated=False.

Current verification: repository main is e54f48a5117c9c10b60f15f6941d8fc7f909d7e1, tree a0f37f103d87b5fa762a7cc1f15bdf26861ca5dc. PR #12 ran the 72-test suite successfully on Python 3.11/3.12/3.13 in GitHub Actions run 34054304431; the merged main tree is exactly the audited candidate tree.

Release evidence: v0.2.0 is tagged at ed3bb2684743376fdf2769ee378ca614c913e3d4; that exact release tag passed source installation, the 16-test suite, and outside-checkout import verification on Python 3.11, 3.12, and 3.13 in GitHub Actions run #11.

Boundary: sanitized reference implementation; no private production state, credentials, provider IDs, or deployment configuration.

Independent public engineering activity. These links do not imply OpenAI employment, affiliation, endorsement, or maintainer status. Authorship and merge state are stated explicitly per item.

• Authored PR #730 — optionally consume task prompt files, implementing the smallest opt-in fix proposed in issue #622.
• The patch adds --prompt-file-consume with READ → DELETE → DISPATCH ordering, preserves ordinary --prompt-file behavior, and rejects consume mode without a prompt file.
• Validation on the exact upstream base: RED 1/3 → GREEN 3/3 focused runtime regressions, 28/28 non-runtime tests, Node syntax check, TypeScript compile, version check, and git diff --check PASS. Upstream workflow execution is currently gated on maintainer approval for fork Actions; I do not claim merge or acceptance.
• An independent public reviewer later endorsed the READ → DELETE → DISPATCH ownership order and the three regressions. I classify that only as independent technical review; formal maintainer/OpenAI status was not verified.

• Authored PR #885 — clarify Bedrock Mantle model-family API roots, a documentation-only upstream contribution tied to issue #812. The change preserves the existing /openai/v1 default and documents the explicit BaseURL path for model families whose AWS model card requires /v1.
• I do not claim merge or maintainer endorsement unless the upstream PR state later proves it.

• Authored PR #4868 — reset compaction response-chain state after a successful pop_item(), fixing issue #4867. Current head 84508a447c8fea2b7a1f2b34a27c411bd21bb09d is the upstream-main synchronization head after the reviewed implementation at 5702cf2d…; the PR remains limited to the compaction-session implementation and tests. Fresh PR validation reports 69 focused tests and 217 memory tests passing, plus Ruff, mypy, Pyright, and git diff --check. Exact-head upstream Actions run 34045087954 remains action_required with 0 jobs, so I do not label it CI PASS or FAIL, and no merge or acceptance is claimed.
• Independent current-head review: an upstream contributor rechecked 84508a4 after the upstream-main sync and reported that the two PR paths are byte-for-byte unchanged from reviewed 5702cf2; their previous concern remains resolved. I classify this only as independent technical review—not maintainer approval, CI approval, merge, OpenAI endorsement, or employment.
• Authored #4747 — PTY teardown can be abandoned after registry removal, focused on cancellation-safe resource ownership after registry removal.
• Authored #4749 — PTY startup cancellation can leak unregistered resources, focused on the pre-registration ownership boundary.
• Reviewed PR #4750 and PR #4751 around cleanup ownership, cancellation propagation, and regression coverage.

• Reviewed PR #3780 around fail-closed CI dependency-gate ordering, scheduler semantics, and non-success dependency states.
• Publicly corrected a stale review blocker after re-reading the exact head revision.

• Revalidated the code-mode tool-output boundary reported in issue #42367, authored by another contributor.
• Published a concrete reference implementation in my fork: fscfede-beep/codex@9e70016.
• Published and merged RUMBO PR #28 — Codex thread scope evidence, a sanitized fail-closed scope-binding probe with 38/38 local regressions and exact-head privacy, scope-binding, and Vercel checks passing; no upstream mutation or root-cause claim.
• I do not claim upstream merge, endorsement, or Rust compile/test PASS for that reference commit.

• Async Resource Ownership Under Cancellation — ownership transfer, cancellation-settled teardown, exception precedence, attempt-all cleanup.
• Fail-Closed CI Gates — required-job scheduler semantics, dependency failure propagation, pre-checkout guards, reviewer self-correction.
• Tool Output Policy Boundaries — caller-requested budgets, model truncation policy, and context-safety boundaries.

• agent harnesses and long-running execution;
• async lifecycle and cancellation safety;
• MCP, plugins, connectors, and tool routing;
• permission and policy boundaries;
• deterministic evals and regression testing;
• canonical-state recovery and effect verification;
• verifiable control planes for consequential agent actions.

• RUMBO IA repository — human-controlled AI CRM and workflow automation with public privacy and verification gates.
• RUMBO Guardian — privacy-first, local-first security intelligence with explainable scoring and a tamper-evident SHA-256 Evidence Ledger.
• VAE Bindings — public privacy-preserving commitments and GitHub attestations for agent work-unit bindings.
• RUMBO IA website — product surface for small businesses in Latin America.
• @RumboAGI on X — public product, evidence, and engineering updates.
• Technical portfolio — selected work in AI workflow and agent reliability.

An agent is not reliable merely because it selected the right action. Reliability requires the authorized action, the executed action, the observed effect, and the evidence about that effect to remain consistent across failures.

I deliberately distinguish authored issues, reviews, reference implementations, upstream code authorship, merge state, endorsement, employment, and production status.

I am open to engineering roles and technical collaboration in agent reliability, developer tooling, applied AI, and agent infrastructure.