How to build responsible AI through agentic AI compliance & safety (Part 3)
You can build an AI agent with crystal-clear logic, rigorously-tested flows, and tightly-defined controls—but once it’s in production, unpredictability takes over. Customers ask unforeseen questions. AI hallucinations occur. Edge cases appear in ways no checklist can fully anticipate.
Previously, we discussed how AI observability helps you monitor and understand LLM agents, while AI governance brings structure and accountability across teams. However, to effectively mitigate AI risk, organizations need mechanisms to detect and respond to unexpected outcomes as they happen.
This is why Sendbird's third layer of AI trust is AI compliance and safety.
Since AI transparency and AI governance alone aren’t enough to ensure AI is operating responsibly, teams need a system that can detect risky behavior, provide guardrails, and keep your brand safe—even when conversations go off script.
How to choose an AI agent platform that works
What does responsible AI look like in practice?
Responsible AI is rooted in your principles and policies for AI—but it’s proven in practice. It's how you address the way your AI agents behave in front of customers, minute by minute, interaction by interaction. For example:
If your AI agent lies to a customer…who finds out first?
If it violates policy…can it be intercepted in time?
If it goes off the rails…how fast can you respond?
This is where AI responsibility gets real. Because in production, risks aren’t theoretical; they have real consequences. Whether it’s an unintended disclosure, a policy violation, or an AI hallucination, the result is the same: a user experience that doesn’t align with your standards.
Having mechanisms for immediate real-time intervention prevents small issues from escalating into costly incidents. This responsiveness ensures AI responsibility.
How does Sendbird support AI compliance and safety?
Sendbird brings AI safety and policy enforcement into the runtime layer. It provides a suite of purpose-built AI safety tools that automatically detect and address unwanted AI behavior in production, helping teams keep their LLM agents aligned with safeguards, regulations, and user expectations. Let’s dive into it.
AI agents' safeguards and real-time monitoring
Sendbird AI includes configurable safety guardrails that allow teams to define and enforce how AI agents should respond. These can include:
Preventing the use of banned phrases or brand-inconsistent language (like a competitor’s name)
Flagging harmful content
Intercepting erroneous behavior that falls outside of approved execution patterns
These AI safeguards act in real time, ensuring that agent responses remain within safe, predictable boundaries.
Automated AI hallucination and risk detection
AI agents can generate convincing but incorrect content, especially when inputs are ambiguous or unsupported. Sendbird monitors for:
AI hallucinations and inaccurate responses
Risky or misleading statements
Gaps between the agent’s logic, your internal policy, and its output
When flagged, these messages can be routed to configured systems via webhooks or to Sendbird's AI agent dashboard for immediate human review to minimize potential risks.
PII checks and safety
Sendbird helps AI trust & safety teams enforce data privacy, protection, and compliance standards through:
Customizable PII detection and redaction
Monitoring of sensitive data (e.g., SOC-2, PCI)
Enforcement of internal and external policies
Whether it’s GDPR, HIPAA, or organization-specific safety rules, these checks help ensure the agent remains compliant with evolving regulations.
Localized AI agents
Global deployments introduce new complexity. What’s compliant and safe in the US may not be appropriate in Europe or elsewhere. This is why Sendbird AI supports the creation of localized AI agents that:
Dynamically adapt to any language, country, or context
Use regional knowledge, API services (tools), and AI agentic workflows
Support popular local channels for conversational AI engagement (e.g., SMS, Email, WhatsApp, Kakao)
With Sendbird, you can deliver a nuanced, brand-appropriate, human-like experience in every market with AI localization.
Safe AI agent deactivation
Even well-trained AI agents can go off script. If an issue escalates—or if proactive review is needed—Sendbird enables teams to safely pause an AI agent without deleting it. Teams can:
Instantly take agents offline while preserving all configuration, history, and logic for review
Quickly investigate the root cause of what went wrong without risking losing customer trust
Redeploy the AI agent fast once safe and compliant
5 key questions to vet an AI agent platform
Next: Why and how to build responsible AI from the start?
AI compliance and safety are essential for demonstrating that AI is being deployed responsibly. By enforcing policies, respecting user privacy, and monitoring for risky behaviors, organizations can show AI accountability and elevate AI agents from experimental tools to enterprise-ready systems.
But enforcing policies alone isn’t enough. When safety mechanisms are triggered too often, they add to the operational burden. True AI trust begins earlier: in how AI agents are designed, tested, and validated. With rigorous AI agent evaluation before and after deployment, organizations can reduce dependence on AI safeguards and deliver agents that work (and have the metrics to prove it).
In the next post, we’ll look at how Sendbird AI supports AI agent evaluation and testing. From automated test sets to AI hallucination testing, AI agent grading systems, and AI environment control—building AI trust has to start at design to be scalable.
⏭️ So, in this next post, learn how to move from responsible AI to reliable AI (the fourth AI trust layer).
👉 If you’re curious to experience the Trust OS for AI agents of Sendbird, contact us.
