Defensive publication + clean-room reference implementation. Establishing dated, enabling public prior art over a mechanism that mines raw agent-fleet transcripts to discover untracked work initiatives — work that emerged rather than was assigned — using negative-set-conditioned LLM extraction and seen-count reinforcement.
Publication date: 2026-07-03 · Author: Gustavo Assuncao, PhD · Publisher: Gus IT LLC (Florida, USA)
When you run a fleet of autonomous LLM engineering agents, work is constantly initiated without ever being chartered. An agent chases a root cause and quietly starts a substantive multi-hour initiative; the only durable trace lives in its raw session transcript, never in your tracker. Your portfolio, burndown, and capacity models go systematically blind to a growing slice of what the fleet actually does.
This repository discloses — as public prior art — a mechanism that fixes that. On a fixed cadence it:
- Builds a negative set from the union of your tracked project-of-record and your existing proposals ledger, normalized to canonical slugs.
- Condenses a sliding window of raw per-agent session transcripts into a bounded, recency-biased, per-agent-quota'd corpus (with a channel to inject orchestrator work that isn't in the transcript store).
- Prompts an LLM with the corpus and the negative set, instructing it to conservatively extract only new, substantive initiatives as strict JSON — each with an owner and an evidence pointer.
- Reconciles survivors into a slug-keyed proposals ledger: first sighting inserts, re-sighting bumps a
seen_countandlast_seen_at. Recurring signal accumulates confidence; one-off chatter decays out of the sliding window on its own.
The result is a continuously-refreshed, ranked list of candidate untracked initiatives — named, owned, evidenced — offered to your portfolio process for promotion, dismissal, or merge. It is a portfolio dark-matter detector for autonomous fleets.
The individual ingredients (cron, LLM extraction, negative prompting, slug dedup, upserts, sliding windows) are commodity. The combination — reconstructing named/owned/evidenced initiatives from raw agent transcripts, subtracting the project-of-record, and using cross-run recurrence as a self-maintaining confidence signal to catch emergent work — is a narrow, useful, non-obvious aggregate: exactly the kind of thing that gets patented. We publish it openly so it stays free for everyone to practice. This is a defensive publication: it is intended to constitute enabling public prior art as of 2026-07-03.
Prior-art risk is assessed MEDIUM; see docs/PRIOR-ART.md.
| Path | Contents |
|---|---|
DEFENSIVE-PUBLICATION.md |
The full ~5,000-word disclosure: mechanism, data model, worked example, prior-art delta, and defensive claims. Start here. |
docs/FIGURES.md |
Mermaid diagrams (pipeline, condensation, reinforcement, reconciliation, lifecycle). |
docs/PRIOR-ART.md |
Real references, delta table, and the honest novelty nub. |
docs/OPEN-SOURCE-APP.md |
How the reference maps to a deployable open-source app + AKS/CronJob sketch. |
src/ |
Clean-room, dependency-free, offline-runnable Node.js (ESM) reference. |
No dependencies, no network, no credentials. Requires only Node.js (ESM).
cd src
node example.jsexample.js reproduces a deterministic three-run trace: a durable recurring initiative climbing to seen_count = 3, an emergent-then-recurring initiative climbing to 2, and a one-off frozen at 1 and going stale — demonstrating both negative-set exclusion and seen-count reinforcement. The LLM is an injectable stub; the datastore is an in-memory store implementing insert/bump upsert semantics.
The reference is intentionally decomposed along the seams a real operator must fill: an injectable model function (llm(prompt) -> text) and an injectable datastore (tracker, intake, transcripts). It contains no proprietary code, no real provider calls, and no secrets. Empirically-tuned production constants (corpus caps, per-agent quotas, item limits, thresholds) are not disclosed — they appear as named config with illustrative defaults and are marked [WITHHELD — trade secret]. The mechanism is fully enabled without them.
Dual-licensed:
- AGPL-3.0-or-later — free/open use under the terms of the GNU Affero General Public License, version 3 or later.
- Commercial license — for organizations that cannot accept AGPL terms, a commercial license is available from Gus IT LLC. Contact gus@gusit.de.
The defensive-publication text is released to the public to serve as prior art. Third-party names/marks referenced are the property of their owners. Nothing here is legal advice.
© 2026 Gus IT LLC (Florida, USA). Author: Gustavo Assuncao, PhD.