The timeline compression that hit software development is here for legal work
AI generates contract provisions faster than you can review them. Creation isn't the bottleneck.
MCP Server Security: What You Need to Know
The worst case: prompt injection tricks your agent into handing over its own credentials. Attackers bypass the AI entirely and access your systems with the agent's full authority.
The power vs. control tradeoff in workflow design (now with Agents!)
Agents give you power—the autonomy and flexibility to handle ambiguous or dynamic tasks. Workflows give you control—the structure, reliability, and traceability you need for predictable, auditable processes.
Your Helpful AI Agent Has a Dark Secret: Security Risks
Agents asking for too many permissions is bad. Fake servers stealing data is worse. But the real nightmare? Prompt injection that tricks your agent into handing over its own credentials.
Agents of change
AI agents can do real work or generate chaos. The difference isn't capability—it's human judgment.
Agents on Rails
The real constraint on agentic AI isn't model capability—it's governance infrastructure. Organizations treat agentic platforms as LLM deployment vehicles when they need complete enterprise systems with guardrails, evaluation layers, and audit mechanisms built in.
MIT predicted AI failure, but agents are already running in production
G2 data shows 60% of companies have AI agents in production with under 2% failure rates—contradicting MIT predictions of 95% project failure. For legal teams, this means governance frameworks can't wait for academic consensus when systems are already deployed.
The coordination gap between building agents and controlling them
Most companies are building autonomous AI capabilities faster than they can deploy them safely. The gap shows up in identity systems that can't handle agent credentials, APIs built for humans rather than machines, and costs that spiral when agents loop endlessly.
