Agentic AI systems are rapidly moving from text generation to tool use, code execution, business operations, and external-state actions. Traditional controls such as prompt guardrails, egress firewalls, and security scanners address only part of the risk. AEGIS proposes a broader operating discipline: every agentic action must carry authority scope, source-control provenance, quality evidence, drift visibility, rollback readiness, and human approval where consequence demands it. This technical note unifies the AEGIS design evolution from spend governance and hard-gate promotion to fleet-level classification, source-control provenance, and AEGIS-Q quality evidence masks. The document synthesises six design sessions spanning April–May 2026 and is structured using the SHASTRA/YUKTI/VIVEKA knowledge architecture: SHASTRA (what is invariably true), YUKTI (how experts reason), VIVEKA (pre-computed inferences). Three expansions of the product thesis are traced: (1) Spend Governance — Agent Budget Attestation (ABA-v1) protocol, human heartbeat state machine, spawn governance rules, and anomaly detection; (2) Fleet Governance — HG-group taxonomy (HG-1 through HG-2B-financial), Five Locks for financial services, the batch factory promotion pattern, Ten Fleet Laws, and a platform solution replacing bespoke per-service scripts; (3) Evidence Operating System — qualityₘaskₐtₚromotion (16-bit, two time horizons), assertQualityEvidence () enforcement function, eight-type drift taxonomy (policy, source, codex, quality, risk, authority, docs, schema), and three buyer packages. Central claim: governance gets AEGIS into the door; quality capture makes it a platform. The moat is not the firewall — it is the evidence chain that survives the agent, the session, the model upgrade, and the audit. AEGIS does not ask whether the agent sounded right. It asks whether the evidence survived.
Anil Kumar Sharma (2026) studied this question.