Recover and Requalify an Interrupted AI Agent Runtime
Reconcile interrupted durable state and side effects, conditionally recertify tool access and fallback behavior, then calibrate escalation controls before resuming an AI agent runtime.
Curated prompt sequences
Workflows
Use Amo.ng prompts in a clear order, with practical guidance for what to provide and what to carry into the next step.
Assure an AI System Change for Production Release
Reconcile the deployed baseline, gate a proposed AI system change, calibrate acceptance thresholds, test adversarial and production coverage, and detect misleading evaluation proxies before release.
Recover a Laravel Payment Checkout Failure Safely
Diagnose and correct a Laravel checkout, webhook, or payment-state failure, build payment-specific test evidence, review security and code risk, and prepare controlled release and rollback gates.
Plan a SaaS Portfolio Consolidation Decision
Reconcile application usage and ownership, evaluate true portfolio redundancy and dependencies, prepare vendor renewal or exit paths, and issue an evidence-backed consolidation decision.
Assess Enterprise Knowledge and RAG Readiness
Govern enterprise knowledge freshness, source authority, retrieval evidence, entitlements, and sensitive-context boundaries before expanding or releasing a RAG capability.
Make an AI Initiative Value and Scale Decision
Reconcile an approved AI value case to realized evidence, diagnose value leakage, calculate accepted-outcome unit economics, and issue a Scale, Hold, Redesign, or Stop decision.
Verify an AI-Generated Software Change for Release
Reconcile a coding agent’s instructions and completion claims against the actual change set, verify dependency and API assertions, close test-evidence gaps, and prepare controlled release gates.
Review Production AI Evaluation Reliability
Determine whether evaluation data, automated judges, production comparability, and retrieval experiments provide reliable evidence for an AI release or operating decision.
Investigate an AI Agent Security Incident
Reconstruct an AI agent incident, trace delegated authority and sensitive context, conditionally investigate memory or RAG authorization, and prepare evidence-based containment and recovery gates.
Produce an Evidence-Traceable Literature Review
Refine a defensible research question, screen and extract evidence, synthesize the corpus, audit citation currency, and trace material conclusions to original evidence.
Build an Evidence-Grounded Marketing Campaign and Measurement Plan
Turn customer objections, competitor evidence, and verified proof into approved positioning, an execution-ready campaign, operational QA, and a controlled experiment plan.
Design a Governed AI Agent Architecture and Accountability Model
Turn an already-approved business use case and verified process evidence into an AI agent architecture with explicit roles, handoffs, authority boundaries, role-based review, escalation, and recovery controls.