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LaunchDarkly launches AgentControl to give engineers real-time control over AI agents in production

LaunchDarkly released AgentControl, letting engineering teams change AI agent behavior in production without redeploying code. Teams can adjust prompts, parameters, and model configs on the fly when performance issues arise.

1 min read
Management

LaunchDarkly Introduces AgentControl for Real-Time AI Agent Management

LaunchDarkly released AgentControl, a tool that lets engineering teams modify AI agent behavior during runtime without redeploying code. The solution addresses a core operational challenge: managing agent configurations and performance across different models and prompts in production environments.

Teams can now intervene instantly when agent behavior drifts or underperforms. Instead of waiting for a new deployment cycle, managers can adjust agent settings on the fly and see results immediately.

What the tool does

AgentControl combines real-time intervention with operational oversight layers. Engineering teams gain the ability to tweak agent parameters, switch between prompts, or adjust model configurations without downtime. This matters because AI development cycles move faster than traditional software, and production issues require quick fixes.

The platform integrates with existing deployment infrastructure, meaning teams don't need to rebuild their stack to use it.

Market context

ServiceNow rose 8.8% to $103.42 on the day of the announcement, following a call with investors about its robotics business. Microsoft closed at $423.54, up 0.4%. Alphabet finished flat at $396.94, near its 52-week high. Apple declined 0.8% to $297.84.

Why this matters for managers

As AI agents move into production, control becomes a management problem, not just an engineering one. Teams need visibility into agent behavior and the ability to course-correct without technical friction. AI for Management professionals should understand how runtime control tools reduce operational risk and speed up decision-making.

For teams deploying AI Agents & Automation, this type of infrastructure is becoming standard. The ability to adjust behavior in real time shifts agent management from a deployment problem to an operational one.

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