ENGAGE · TEAM
Lead · Enterprise AI and Agentic Systems
ExperienceNearly 20 years across software engineering and architecture.
LinkedInAnkur leads Enterprise AI and Agentic Systems at Bugni Labs. He operates as both architect and hands-on engineer: shaping technology direction and system architecture, and building the capabilities that make AI-native systems dependable in production. His work covers the end-to-end architecture of enterprise AI and agentic systems, including agent and multi-agent architectures, orchestration and tool use, context and memory, evaluation and observability, governance, and integration with enterprise systems. His financial-services experience includes architecting and delivering AI systems across regulated banking, from fraud and financial-crime use cases to agentic customer journeys and shared enterprise capabilities.
Largely one argument worked from different angles: an AI agent is only as trustworthy as the context and the controls around it. The recurring threads are the context layer itself, runtime policy enforcement over written policy, and grounding, evaluation and budgets treated as engineering controls rather than prompts. The applied pieces come out of financial crime, KYC and screening work inside a regulated bank.
12 pieces · 12 Perspectives
AI agent controls only count if you can test them. A context layer is really a control layer, with every rule tied to a passing test and a line of defence.
Read perspectiveA context layer architecture sits between agents and systems, enforcing policy before the agent reasons and producing audit-ready evidence. Here's how it holds.
Read perspectiveRuntime policy enforcement, not documentation, governs AI agents. Writing a rule down is easy. Making an agent physically unable to break it is the work.
Read perspectiveThe market is converging on one context layer for AI under a dozen names, and the one part that actually governs agents is yours to own. Here is the map.
Read perspectiveAI agent governance needs five kinds of context to act correctly and accountably. Evidence and authority are the two a regulated business is audited on.
Read perspectiveAn AI context layer gives agents the meaning, permissions and evidence to act safely. Here is what it actually is, minus the vendor hand-waving.
Read perspectiveEnterprise AI agents keep stalling before production. The blocker is not the model. It is trustworthy context and control enforced the moment an agent acts.
Read perspectiveIn regulated automated decisioning, a reproducible record beats a marginally smarter model, and keeping AI out of the verdict is often the right design.
Read perspectiveAI grounding fails when it is requested rather than enforced, and traceability proves provenance, not truth.
Read perspectiveAutonomous AI agents need hard limits on steps, tools, time and spend, enforced by code outside the agent's control.
Read perspectiveAI cost control is usually chased through cheaper models. The larger saving comes from resequencing the pipeline so cheap checks precede expensive work.
Read perspectiveAI vendor lock-in is usually fought as a pricing negotiation. In regulated institutions it is an architecture and concentration-risk decision the CIO owns.
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