AI agents are built to complete tasks autonomously, not to recognise that a conversation has drifted into grooming, coercion, or exploitation of a minor. Tuteliq is the safety layer that screens agent inputs and outputs for the harms agents were never trained to catch.
An AI agent is built to act on a user's behalf, to book, browse, message, and automate. Tuteliq tells you when that agent's conversation with a 12-year-old has turned into a six-message grooming arc, before anything explicit is said. They're not competing for the same job, and using both is the right architecture: the agent as the autonomous worker, Tuteliq as the deep behavioral and KOSA-aligned safety layer watching over it.
Pick AI Agents if
- You need an agent to plan, browse, call tools, or automate a workflow
- Task completion and reasoning are the core job, not safety classification
- Your agent operates in a low-risk, adult-only context
- You already have a separate moderation/safety layer in place
Pick Tuteliq if
- Your agent talks to or around minors and needs grooming detection
- You must screen agent outputs before they reach a vulnerable user
- KOSA, UK OSA, or EU AI Act obligations apply to your agent's behavior
- You need an audit trail and incident reporting for what the agent did
Feature-by-feature
Capability
Tuteliq
AI Agents
Autonomous task execution
Multi-turn grooming detection
Coercive control / manipulation patterns
Synthetic CSAM (deepfake) detection
Output safety screening for minors
Age verification (document + liveness)
Cultural calibration per language
EU data residency (default)
DSA Statement-of-Reasons exporter
Incident reporting & audit trail
Moderator console
Things you'll hear from AI Agents
And our straight answer.
Our agent already has safety instructions in its system prompt.
Prompt-level guardrails are easily bypassed by jailbreaks and degrade across long, multi-turn sessions, exactly where grooming and coercion live. Tuteliq independently inspects the actual conversation for behavioral risk, so safety doesn't depend on the agent obeying its own instructions.
Won't adding a safety layer slow our agent down?
Tuteliq's P95 is 1.1-1.5s typical and runs in parallel with your agent's reasoning. For minor-facing agents, regulators expect this trade-off, and we expose async webhooks where latency is critical.
Can't the agent's underlying model just self-moderate?
A general-purpose agent model is optimised for task completion, not for spotting a 34-year-old grooming a 12-year-old across six turns. Detecting that pattern is a specialised job, which is exactly what Tuteliq is built for. The two are complementary, not competing.
The verdict
Use AI agents to get work done. Add Tuteliq as the independent guardrail that watches what your agent says to and around minors, the behavioral and compliance layer agents weren't built to provide.