The Pipelines module models AI agent work as observability (not execution). The Pipeline -> PipelineStep -> PipelineJob hierarchy in PostgreSQL lets you track every stage of agent work: from research, through coding, to wrap-up. Job logs are stored as wiki pages, status propagates upward - a failed job pulls its step and pipeline down. The dashboard refreshes every 15 seconds, and is_stale() flags pipelines stuck for more than 6 hours.
Each pipeline has steps (research, coding, wrap-up), each step has jobs. Status propagates upward - failed job -> failed step -> failed pipeline.
Every job has its own wiki page with a full log (content in MinIO). Pages grouped under the 'pipeline-logi' parent, excluded from RAG - they don't pollute search results.
The pipeline list and detail view refresh every 15 seconds via HTMX. Durations are computed on the fly (finished_at - started_at or now() - started_at for active jobs).
is_stale() flags pipelines in running status with no updates for more than 6 hours. Computed at read time, no cron job needed.
The dashboard is read-only - pipelines are created by skills via MCP, not from the UI. AI agents have full control through 9 dedicated MCP tools.
When LLM Wiki is enabled, finishing a pipeline appends an entry to wiki-log. Best-effort - a pipeline MCP error never blocks the agent's work.
The skill calls create_pipeline via MCP - a ticket_work pipeline automatically gets 3 steps: research, coding, wrap-up.
create_pipeline_job registers a new job in a step (e.g. 'Source code analysis' in the coding step). Status: pending -> running.
append_job_log appends markdown to the job's wiki page. Each call adds another section - the complete history of agent work.
update_pipeline_job(status='success'/'failure') closes the job, sets finished_at, and propagates status up to the step and pipeline.
Pipeline view with step columns and job cards. Statuses, durations, and links to wiki logs - everything in real time.
Agent calls finish_pipeline when done. Final status aggregated from steps if not provided. Optional wiki-log entry when LLM Wiki is enabled.
9 MCP tools for full observability of agent work - creating pipelines, reporting jobs, appending logs, and closing out. All operations are best-effort - a pipeline error never blocks the agent's work.
Available MCP tools
create_pipeline
Create a pipeline (ticket_work: automatic research/coding/wrap-up steps).
create_pipeline_job
Add a job to a step with name, description, and starting status.
update_pipeline_job
Update job status and summary. Propagates upward: job -> step -> pipeline.
append_job_log
Append markdown to the job's wiki page. Creates the page on first call.
finish_pipeline
Close the pipeline with final status (aggregated from steps if not provided).
list_pipelines
List project pipelines with status and type filters plus pagination.
get_pipeline
Full pipeline tree: steps, jobs, timestamps, statuses, and log links.
get_pipeline_job_log
Fetch the full job log as markdown from the linked wiki page.
clean_pipeline_logs
Delete the wiki pages holding pipeline job logs for a sprint (cleanup after close).
Error tracking
Agile project management
URL health checks
Cron job monitoring
Documentation & RAG search
Dependency graph
Work analytics & PDF export
Cross-project billing & attachments
Personal cross-project scheduling