a21.ai blog
All you need to know about Generative AI
Agentic Engineering 101: Roles, Contracts & Failure Modes
Agentic AI is reshaping how organizations build intelligent systems that act autonomously, but success hinges on treating it as an engineering discipline rather than a plug-and-play technology. This guide introduces the foundational elements—roles for human-AI collaboration, contracts for reliable interactions, and common failure modes to anticipate and mitigate.
Why Agentic AI Pilots Stall—and How to Scale Without Losing Control
In platform operations, agentic AI promises to transform reactive workflows into proactive, autonomous systems that handle everything from incident resolution to resource optimization. Yet, most pilots never escape the proof-of-concept phase, trapped by misaligned expectations, governance gaps, and integration hurdles.
From SOPs to Supervision: Training Legal Teams to Work with AI Systems
The legal profession is undergoing a profound transformation as AI tools move from experimental pilots to core components of daily practice. Traditional standard operating procedures (SOPs) — once the bedrock of consistency in research, drafting, and review — are giving way to a new paradigm: active supervision of intelligent systems.
What an Enterprise AI Operating Model Actually Looks Like
Executives ask the same question in different words: we have pilots and proofs that look promising, but they don’t consistently move into production, into measurable value, and into audit-ready, repeatable practice. The missing piece is rarely the model. It’s the operating model — the decisions, the handoffs, the guardrails, and the incentives that turn a one-off experiment into a durable capability.
How Boards Should Think About AI Risk in 2026
Boards must treat AI risk the way they treat financial, legal, and cyber risk: as a board-level, recurring agenda item that combines opportunity with measurable guardrails. Done well, AI governance preserves competitiveness while reducing operational, regulatory, and reputational downside; done poorly, AI programs create brittle systems, audit gaps, and outsized exposures. This post gives boards a practical playbook for oversight in 2026: what to ask, what to measure, and how to convert governance into a competitive enabler.
AI in Regulatory Submissions: Speed Without Risk
Regulatory filings are the bottleneck that turns product momentum into calendar risk. For life-sciences leaders, a faster submission is more than a headline metric — it’s earlier market access, earlier revenue, and fewer months spent in regulatory limbo. But speed without controls yields risk: sloppy citations, missing exhibits, and untraceable edits invite rework, inspection headaches, and reputational damage.
Medical Affairs Knowledge Graphs Powered by Retrieval-Augmented Generation
Medical affairs teams sit at the intersection of evidence, clinical practice, and commercialization. They must surface safety and efficacy signals, respond to field questions with defensible citations, and support market access and post-market commitments — all while swimming in an ever-growing flood of trials, registries, labels, payer policies, and real-world evidence. Traditional search and manual synthesis are increasingly brittle: slow to scale, hard to audit, and risky when the evidence base moves quickly.
Legal Ops as a Data Product: From Contracts to Insights
Legal teams no longer only draft and redline. The best legal operations organizations turn contracts into living data products that power faster decisions, measurable compliance, and new revenue opportunities. Treating legal output as a product—discoverable, versioned, audited, and instrumented—changes the conversation from “How do we keep up?” to “How do we scale legal judgment across the business?”
Fraud Detection That Explains Itself to Regulators
Fraud is an expensive, reputational, and regulatory risk for insurers. Modern detection systems can flag suspicious claims with high accuracy, but that alone isn’t enough. Regulators, auditors, and internal reviewers increasingly demand evidence — a clear, auditable trail that shows why a claim was flagged, who reviewed it, and which rule or data point justified the action. In short: fraud systems must not only be effective, they must be explainable.
