Many AI agent stacks focus on coordinating workflows (orchestration). For systems with real-world impact, a separate "control plane" is essential. This layer independently validates and authorizes proposed actions against current policies and system state, preventing unsafe outcomes that agents alone might cause.

Purely agentic systems can be unpredictable. A hybrid approach, like OpenAI's Deep Research forcing a clarifying question, inserts a deterministic workflow step (a "speed bump") before unleashing the agent. This mitigates risk, reduces errors, and ensures alignment before costly computation.

Lindy dramatically increases agent reliability with a "validator" system. Before an action is taken, a second LLM call acts as a judge, checking the proposed action against an extensive prompt or checklist. Even a simple "Are you sure?" prompt provides a significant reliability bump.

Instead of manually writing complex security policies for agents, run the agent in an audit mode to capture its network traffic. Then, use an LLM to analyze this traffic and automatically suggest a comprehensive, baseline security policy based on observed behavior.

Baking refusal safety mechanisms directly into base large language models degrades their utility for legitimate enterprise workflows like penetration testing and biological research. By un-refusing the base reasoning model and running a separate, millisecond-latency moderation model on top, enterprises gain an inversion of control. They can programmatically define company-specific safety policies without being blocked by native model refusals.

Relying solely on tool constraints within an agent is insufficient, as it can always find ways to cross the network boundary. A more robust approach is to use a proxy that intercepts all network requests, allowing for policy enforcement at the network level.

Instead of simply blocking unexpected agent behavior, Eve Security's platform actively questions the agent to understand its intent. This 'interrogation' process cross-references the agent's answers with other systems to determine if a new behavior is legitimate or malicious, enabling more nuanced control.

The OpenAI/Hugging Face security breach proves that humans are too slow to manage AI safety. The solution is to deploy 'guardian models'—AIs that are equally intelligent as the agents they monitor. These guardians will observe agent actions in real-time, flagging or blocking unsafe behavior before it causes harm.

Simply governing the initial prompt is insufficient for autonomous agents. The critical point of control is when the AI decides to take an action—running a function or accessing a database. Effective governance must intercept these actions to apply policies before they execute.