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Only one in five companies has a mature governance model for AI agents. Those companies are deploying agents in higher-value workflows because they have the confidence to do...
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80% of companies exceed their AI cost forecasts by 25% or more. The primary driver is not model pricing. It is architectural waste: ReAct agents that retry failed actions, reload context at every step, and burn tokens on redundant reasoning loops.
Insurance claims processing is one of the most document-intensive workflows in any industry. AI agents can compress that timeline while enforcing compliance at every step.
MightyBot's compilation pipeline transforms plain English policies into hybrid execution plans that combine deterministic code paths with structured LLM calls.
Visual workflow builders look fast on day one. But at enterprise scale, the maintenance burden, brittleness, and governance gaps make them one of the most expensive architectural decisions a company can make.
Progressive autonomy is the operating model where AI agents graduate through increasing levels of independence based on demonstrated performance. The three levels are Audit (AI executes, humans review everything), Assist
Most AI failures don't happen during the pilot. They happen six months later, when the pilot "succeeds" and the organization tries to run it for real.
Summary: Most AI agent frameworks use ReAct loops that hallucinate tool calls, burn tokens, and produce non-deterministic results. MightyBot compiles plain English policies into hybrid execution plans that combine LLM re
A CISO's evaluation framework for AI agent vendors: six security requirements beyond SOC 2, shadow AI risk mitigation, and EU AI Act compliance — what to demand before allowing AI to process sensitive data.
Non-human identities (NHIs) let AI agents access enterprise systems. With 144 machine identities per human, NHI governance is now critical — especially in regulated industries.
Purpose-built AI document processing for construction lending goes beyond OCR — handling multi-document draw packages through classification, extraction, normalization, and cross-document reconciliation.
RAG retrieves information, but regulated industries need policy enforcement, evidence chains, and auditable decisions. Five gaps between RAG and production-grade AI for financial services.