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Securing AI Agents: Tool Use, Permissions and Boundaries https://WebToolTip.com Published 10/2026 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch Language: English | Duration: 11h 22m | Size: 2.65 GB Build a hardened AI agent bench: MCP, least-privilege tools, OPA policy, sandboxes, tokens, approvals and audit logs What you'll learn Explain what changes when a model can act, and draw trust zones around the model, loop, tools, memory and identity Build a local agent bench with Ollama and a small tool-calling model, with every step of the agent loop logged Write your own MCP server, read exactly what the model sees, and validate tool inputs and outputs against schemas Replace open-ended tools with narrow ones, confine file paths correctly and split read and write servers Put an OPA policy decision in front of every tool call, with argument-level rules tested before they ship Run tools in hardened containers with no network by default, resource limits and throwaway execution Use short-lived, audience-bound tokens and scoped credentials so an agent never carries more authority than it needs Keep untrusted content out of instructions with marking, plan-then-execute and a guard model, and block injected actions by policy Add human approval gates bound to one request, per-user memory isolation, access-controlled retrieval and redaction Enforce budgets, log every tool call, write detection rules, pin MCP servers, threat model the agent and run a kill-switch drill Requirements Comfort with Python and the command line; you can read a short script and run it A machine that runs Docker; Linux is recommended because one lab uses bubblewrap. No GPU is needed: the labs run on CPU