Nathaniel from InfoCap joins this AWS Partner Spotlight with Ingram Micro to unpack what makes agentic AI fundamentally different from both traditional workflow automation and today's generative AI — and why accountability, not capability, is the real challenge in government adoption. Nathaniel explains how InfoCap builds observable, auditable AI systems that reduce case backlogs while keeping humans firmly in the loop on final decisions.Key Takeaways🤖 Agentic AI acts, it doesn't just answer — Unlike earlier AI that generated predictions or answers, agentic AI actually performs the work itself — completing complex casework, applications, and adjudication tasks that previously required a human.🔀 Not the same as workflow automation — Traditional workflows follow predefined "if this, then that" paths; agentic AI operates within guardrails and policies but chooses its own path through a near-limitless set of possible steps.🔍 Accountability is the real bottleneck — The hardest part of deploying agentic AI in government isn't the technology — it's defining who is accountable when an agent's decision is wrong, since "you can't hold an agent accountable."⚖️ Agents do the digging, humans make the call — InfoCap's approach has agents handle the research and due diligence behind a decision (often 95% of the work) while leaving the final, accountable determination to a human.Timeline00:00 – Welcome & introduction00:21 – What makes agentic AI different from recent AI adoption01:10 – How agentic AI differs from traditional workflow automation02:10 – Where work actually gets stuck today03:56 – When does a human need to be in the loop?05:15 – Moving beyond the "black box" — observability & accountability07:29 – How InfoCap builds accountable, transparent automation08:35 – How agentic AI reduces backlogs without sacrificing oversight09:35 – Where InfoCap fits in the government AI landscape