Autonomous, not reckless
The system discovers, inspects, scores, and prepares opportunities without blindly sending emails or spending money. Automation is bounded by settings, caps, cooldowns, route contracts, and review gates.
Agent system
A governed opportunity intelligence system that can start from zero or from trusted source lists, verify zero-source readiness from live Worker settings, advertise a zero-source route map through the Worker catalogue, show a pending-deploy fallback when the Worker route catalogue is older than the Next UI, bootstrap safe source memory, recover when source memory is empty or exhausted through a source-finding fallback playbook, expand into new source candidates with bounded scans, discover deeper pages through robots.txt and sitemaps, generate query-pattern source hints, guide last-resort query recovery with a query-hint recovery brief, resolve human-reviewed search results into candidate sources, inspect public partner/client/supplier-style links from known pages, preserve discovery origin, learn which source paths produce useful opportunities, translate discovery signals into an operator brief with why/guardrail reasoning, judge candidate-source batches with promote/inspect/hold guidance, judge source health with trust/tighten/pause guidance, show whether plans are based on live signals or fallback guidance with checked-time freshness, route operators to zero-source startup when no source-origin signals exist, route operators to the right next section from cycle-mode posture, turn signals into staged safe automation plans, and keep AI/sending behind explicit review gates. It can begin without a supplied source list, build candidate-source memory safely, preserve origin, learn which discovery paths produce useful opportunities, and recommend the safest next move before anything expensive or risky happens.
How it thinks
The agent is designed around a professional intelligence loop: zero-source startup, source discovery, candidate memory, public link graph exploration, review, origin metrics, strategy learning, and adaptive recommendations.
The system discovers, inspects, scores, and prepares opportunities without blindly sending emails or spending money. Automation is bounded by settings, caps, cooldowns, route contracts, and review gates.
A manual source list helps, but it is not required. The system can bootstrap durable seed memory, run tiny bounded scans, rotate methods, and build candidate-source memory before anything becomes live.
It can start from trusted sources or built-in seeds, expand through bounded scans, inspect robots.txt and sitemaps, follow ordinary public relationship links, generate query hints, and learn which paths produce useful sources.
Sources, candidates, opportunities, scores, reviews, runs, and recommendations carry evidence trails so the operator can see why something was found, saved, cooled down, or prioritised.
The safest mode keeps AI calls and sending off while allowing diagnostics, preview, source testing, query hints, sitemap scans, public-link scans, and confirmed candidate saves. Risky actions require explicit settings and confirmation.
Discovery strategies
A robust opportunity agent should not depend on one tactic or one supplied list. It combines zero-source bootstrap, high-trust seeds, public link graphs, sitemaps, and query-result resolving, then learns which path actually creates useful sources.
A new installation can begin without a supplied source list by creating local seed memory before any networked discovery happens.
Durable registries and known domains form the first safe discovery layer when a source list exists.
Known public pages can reveal partner, client, supplier, funding, portfolio, and procurement-style links worth reviewing.
Robots and sitemap files can expose deeper source pages without broad uncontrolled crawling.
Generated search patterns help an operator find useful URLs, then paste them back into the resolver for scoring and dedupe.
Source discovery flywheel
The system should not depend forever on a manually supplied source list, and it should not fail when one is missing. It builds a controlled discovery flywheel that bootstraps, expands, recovers, scores, preserves origin, and learns from what actually produces useful opportunities.
If there is no supplied list, create local seed memory from durable government, grant, procurement, business-support, and creative-funding registries.
Use supplied trusted sources or built-in default registries as the first safe discovery layer.
Run bounded page scans and build candidate-source memory without automatically promoting new sources.
When scans find nothing, fail, or rediscover duplicates, use fallback guidance to rotate method before increasing depth.
Inspect ordinary public links from known pages to find partner, client, supplier, funding, portfolio, and opportunity-style source candidates.
Use robots.txt and sitemap files to uncover deeper public pages that may not appear in a simple source list.
Generate AU/NZ search-pattern hints for digital services, grants, supplier panels, tenders, and creative technology funding.
Open a query manually, paste useful public result URLs, and let the resolver filter noise, score candidates, and dedupe known domains.
Promote candidates only after confirmation. Saved sources preserve their origin, score, strategy, and operator reason.
Use source health, opportunity yield, origin metrics, and strategy scores to recommend where the next safe scan effort should go.
Cockpit
The Operations cockpit exposes the real control surfaces: zero-source startup, source expansion, public link scans, query hints, resolver flows, origin metrics, strategy learning, recommendations, route contracts, and audit rows.
Operating modes
The system grows in stages: first source intelligence and review, then candidate saving, then draft preparation, then controlled sending only after settings, caps, and evidence support it. Zero-source startup belongs in the free-safe stage, not in outreach.
AI off, sending off, zero-source startup, zero-source route-map visibility with pending-deploy fallback, bounded source checks, scheduled source expansion, source-finding fallback playbook, public link graph discovery, sitemap scanning, query-hint generation, query-hint recovery brief guidance, query-result resolving, candidate-source testing, origin metrics, adaptive recommendations, operator brief reasoning, candidate review brief guidance, source health brief guidance, live/fallback/zero-source plan signal status, checked-time freshness, actionable cycle-mode posture, staged safe automation planning, evidence-rich opportunity extraction, calibrated review saves, run audit history, source-health scoring, scoring diagnostics, and review learning.
Continuous source expansion, public partner/client/supplier-style link discovery, sitemap/robots discovery, query-pattern source hints, query-hint recovery brief guidance, human-reviewed URL resolving, fallback source-finding playbook, zero-source readiness checks, zero-source route-map visibility with pending-deploy fallback, candidate preview, candidate review brief guidance, confirmed local save, source testing, source health brief guidance, source health review, operator brief reasoning, next-best-action planning, live/fallback plan signal status, checked-time freshness, actionable cycle-mode posture, dedupe, and inserts only after explicit confirmation rules.
Reviews teach the system which grants, tenders, partner openings, agency signals, and project clues are worth pursuing, while source-origin metrics show which discovery paths produced them.
Reviewable drafts and response notes only after settings, caps, provider rules, source health, origin learning, scoring diagnostics, guardrail checks, and audit records are configured.
Only after approvals, settings, caps, suppression rules, source quality checks, origin metrics, scoring diagnostics, guardrail checks, and run-level audit trail are in place.
Roadmap
This page reflects the current worker state: zero-source startup, source expansion, public link graph discovery, sitemap scanning, query hints, resolver workflow, saved-origin preservation, origin metrics, origin-aware learning, and adaptive recommendations.
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Risk, badges, checklist, compact/full dashboard modes, refresh hints, contract metadata, and self-test.
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Sources can be tracked with state, success/failure counts, cooldowns, quality scoring, notes, created time, saved-source origin, and manual controls.
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The Ops console verifies live Worker readiness, routes no-signal automation plans to start-from-zero mode, highlights the Worker-advertised zero-source route map or a pending-deploy fallback, and explains how to bootstrap candidate-source memory without any supplied manual source list.
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When candidate memory is empty, stale, exhausted, duplicate-heavy, or low-yield, the Ops console now routes the operator through bootstrap seeds, tiny bounded scans, sitemap/robots probing, public-link discovery, and query-hint resolving before increasing depth.
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The query-hints panel now explains when to generate hints, which high-score or unused hint to try first, when to open search manually, and when pasted public URLs are ready to resolve into candidate memory.
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The agent can bootstrap expansion seeds, run bounded seed scans manually or on the scheduled tick, store discovered source candidates, remember quality, and expose candidate memory in Ops without automatically saving live sources.
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Known source domains can be inspected through robots.txt and sitemap files to discover deeper tender, grant, supplier, funding, and opportunity pages under strict caps.
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Known public pages can be checked for partner, client, supplier, funding, portfolio, and opportunity-style links. Useful links are scored and saved as candidates only.
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The system can generate explainable AU/NZ query hints for tenders, grants, supplier panels, creative funding, and digital-service opportunities without searching the web automatically.
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Operators can paste useful result URLs from a query hint; the resolver scores them, filters noise, dedupes known domains, and stores source candidates for review.
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Live sources are split by origin path so operators can see what came from query hints, public link graph discovery, sitemap discovery, bounded expansion, candidate preview, or manual sources.
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Private operations pages and proxies require operator authentication. Settings writes and manual opportunity runs must carry explicit confirmation flags before reaching the Worker.
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A read-only recommendation layer compares source-origin yield, active sources, failures, and strategy scores to suggest where the next safe discovery attention should go.
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The cockpit condenses the highest-value recommendation into one operator brief with what to do now, why it matters, the relevant evidence, and the guardrail that keeps the action safe.
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The cockpit translates origin recommendations into practical operator actions with confidence, risk, payoff, and evidence counters.
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The automation plan states whether it is using live recommendations, has no origin signals yet, is loading, has routed to zero-source startup, or has fallen back to conservative guidance, and shows when that check last completed.
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The automation plan summarises whether the current operating posture is start-from-zero, learn-first, inspect-first, expand-ready, or review-first and links directly to the recommended next section.
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The Ops console turns live recommendation signals into staged start, learn, inspect, expand, and review steps with guardrails for each phase.
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Small recurring expansion scans can run under autonomy caps, source quality, cooldowns, and no-AI/no-email defaults. They store candidate memory only; saving live sources remains confirm-gated.
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Dedicated tables, preview, commit-preview, review decisions, and learning summaries for grants, tenders, partner programs, supplier panels, and contract-style demand signals.
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Operators can preview scored candidate opportunity sources and duplicate flags before any save step.
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The candidate panel now interprets each batch as promote, inspect, hold, or waiting, then selects only fresh recommended candidates under a capped operator-confirmed save path.
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After preview and review-brief guidance, operators can explicitly confirm selected candidate URLs into opportunity_sources through local metadata-only save while preserving discovery origin.
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Saved candidate sources can be filtered, identified with badges/notes, jumped to from the candidate panel, then tested or previewed from the source manager.
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The source health panel now interprets the source mix as trust, tighten, pause or repair, review, or waiting before more discovery budget is spent.
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Review decisions can feed strategy scores and separate shortlisted, watching, needs-research, rejected, duplicate, and archived opportunities.
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Opportunity runs can record lifecycle status, source-level results, candidate rejection reasons, and read-only run detail for the Operations Hub.
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Run audit history can score sources as strong, useful, noisy, weak, or failing and recommend whether to prioritise, monitor, tighten, or pause them.
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Operators can confirm local metadata-only actions such as pause, activate, lower priority, raise priority, and reset source error state.
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Operators can inspect calibration impact, reason counts, source-health influence, review-learning influence, guardrail counts, warnings, and boosted/penalised opportunities.
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Scoring now caps adjustment strength, limits low-confidence boosts, applies ceilings without strong signals, and preserves review floors for strong-fit/urgent opportunities.
System map
The product experience exposes safe parts of the worker clearly: planner state, zero-source bootstrap, source intelligence, public link discovery, query hints, sitemap discovery, resolver workflow, origin metrics, review flow, diagnostics, and audit history.
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