AI agents are moving into production rapidly, performing tasks like triaging tickets, summarizing logs, provisioning environments, running tests, writing code snippets, and answering developer questions. Many agents are deployed informally by developers without review, infrastructure, or ownership, creating latent risks when they interact with real systems. Agents have caused overwritten configurations, noisy retries, and misrouted support requests due to lack of operational controls. Treating agents as first-class deployable units requires structured inputs, constrained actions, guardrails, monitoring, versioning, reproducible decision paths, progressive trust, and extension of IAM, canary deployments, secret rotation, observability, and incident playbooks.
AI Agents are Entering Production Faster Than You Think AI agents are already helping teams triage tickets, summarize logs, provision environments, run basic tests, write code snippets, and answer developer questions. But here's the rub: them are being deployed without formal ownership . A developer experiments with an LLM wrapper, gets useful results, and suddenly it's a critical part of the toolchain. No review. No plan. No infrastructure.
That's a problem. Once an agent starts interacting with your real systems, the risks escalate quickly. Is it These aren't academic questions. Teams are already running into issues where agents overwrite configs, cause noisy retries, or misroute customer support requests-not because they're flawed, but because nobody treated their onboarding as a formal deployment. Remember the Replit.ai incident with the deleted database? Exactly?
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