AI orchestration: from a prompt to an accepted result
A practical design for delegation, evidence and recovery.
Start with a contract
Describe the observable outcome, allowed inputs, writable paths and acceptance command. The orchestrator coordinates a process; it does not turn an ambiguous wish into a reliable result by adding more workers.
Separate execution and acceptance
A worker produces a candidate. Deterministic checks inspect it. A reviewer checks the meaning and the evidence. Completion, compilation and acceptance are different events. Stop conditions are successful control behavior when the task is blocked.
Build one useful loop first
Choose a repetitive process with a reference answer. Keep ownership exclusive and record failed attempts. Expand only after the accepted output and recovery path are repeatable.
Keep exploring
Shared memory without shared mistakesKeep facts, hypotheses and permissions distinct across agent sessions.Engineering that can show its workTVIJO connects operators, bounded agent work and knowledge that can be checked and reused.AGENTORCH: the orchestrator has a concrete jobAGENTORCH names the orchestration role: plan, assign, observe, reconcile and accept.The task packet is the interfaceA good task describes a verifiable outcome rather than an invitation to improvise without limits.AGENTJUDGE: acceptance follows evidenceA reviewer checks the task, the artifact version and each acceptance criterion.Measure cost per accepted resultSmall task benchmarks illustrate why task class and acceptance matter more than a cheap model label.Budgets and outages belong in the state machineA reliable operator view distinguishes working, waiting, failed, blocked and accounting uncertainty.Keep VECTORBASE, evaluate retrievalUse a private source-linked index to find evidence; do not treat search results as authority.SPIDER: evidence collection and useful progressThe first implementation reads an explicit project-evidence list and produces a dated operator digest without model calls.DSHREMBOT and tmux: one operator viewAuthenticated project commands, budget visibility and explicit schedule controls connect the operator to existing execution lanes.The AI worker–Hugging Face incident: learn from controlsThe incident reports support specific engineering lessons, not a controlled ranking of our system.What the supplied AI worker screenshot actually provesThe screenshot records a safeguard-triggered switch in one session; it does not explain the exact trigger. The observation is the supplied screenshot dated 15 October 2026.Training our own model: a gated research directionA proprietary model is an option to test after retrieval, prompts and routing have measurable limits.A bilingual knowledge product with visible limitsPublish useful explanations first, then expand languages when the evidence and workflow are ready.Free learning, custom projects and future challengesTurn evidence-backed engineering into clear offers and a useful developer community.Trust the sender, not a public checksumBind the complete message to an explicitly trusted authority.