Resources
Practical analysis on the governance, architecture, and regulatory questions that come up when deploying AI agents in legal, asset management, and financial services environments. Each post is grounded in primary sources and the work CompleteFlow has done with clients on private infrastructure.
Traditional workflow tools like HighQ, iManage and Litera Transact run repeatable processes well but stall on unstructured work. AI agents handle the judgement-heavy work the templates can't reach. A guide to where each fits in a modern firm.
When an AI agent queries your document management system, whose permissions does it use? For most platforms, the answer is a shared service account. That's a problem.
The EU AI Act deadline for high-risk AI systems is August 2, 2026. Financial services, insurance, and legal firms deploying AI agents must act now on conformity assessments, technical documentation, risk management, and human oversight to avoid penalties of up to 35 million EUR.
How law firms can adopt AI without compromising SRA compliance, legal professional privilege, or client confidentiality. Covers regulatory requirements, the privilege problem with US-hosted tools, and practical private deployment architecture.
A technical analysis of the Model Context Protocol's security model, its gaps for regulated industries, and the governance controls needed to deploy MCP servers in compliance-sensitive environments.
Audit trails, approval workflows, identity, data boundaries, and policy-as-code enforcement: a technical reference for AI agent governance in regulated industries.
A practical reference covering the regulations that apply to AI agent deployments in financial services, legal, and healthcare, from GDPR and FCA Consumer Duty to the EU AI Act and MiFID II.
Most AI agent demos show them booking restaurants. The actual hard problem is deploying agents where every decision needs an audit trail and a wrong answer has regulatory consequences.
