How to Manage and Secure AI Coding Agents

AI coding tools are projected to exceed $13 billion in spending in 2026 as autonomous agents increasingly plan, write, test, and deploy code while calling language models, MCP-connected tools, and other agents. The expanded connectivity creates governance challenges beyond code quality, including insecure generated code, prompt injection, excessive tool permissions, data leakage, unmanaged non-human identities, and uncontrolled token costs; cited research reports limited visibility into developer AI usage and finds that only 55% of tested AI-generated code was secure. Drawing on OWASP’s agentic-application risks and Gartner’s AI TRiSM framework, the discussion argues that policies alone are insufficient and recommends continuous infrastructure-level controls: inventorying agents and their connections, assigning least-privilege identities, applying data and prompt protections, restricting MCP tool access, setting token budgets, and centrally logging activity. It positions AI gateways as a way to enforce these controls across LLM, MCP, and agent-to-agent traffic, while emphasizing that gateways complement rather than replace code scanning, human review, identity management, and other security tools.

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