A working space for agents, automation, evaluation methods, trust layers, compliance research, and product prototypes. Some experiments stay internal; the strongest become public Burhuc products.
Experimental workspace · Built in Luxembourg · Product tracks marked by maturity and evidence
Current public tracks from the lab. Each card is product-specific; AI Labs itself remains broader than any one framework.
Tests a frozen agent-system configuration against a versioned suite. Results describe behaviour observed in that run and do not certify future behaviour, legal compliance, or production safety.
Converts observed failure patterns into controlled remediation candidates. TRAIN results are practice evidence; improvement remains specific to a protected re-evaluation of the frozen system, and broader generalisation is unproven.
Represents the policy-mediated control boundary for protected agent effects: identity, tool calls, writable state, outbound network access, and short-lived secrets. Production effectiveness requires deployment-specific evidence.
Burhuc Labs follows a simple loop: build, test, measure, improve, and decide. The strongest experiments become internal tools or public products.
Fast experiments are allowed. Public claims are not. Each lab track must earn confidence through tests, evidence, deployment gates, and operational review.
Explore agents, automation, research workflows, trust layers, and decision tooling.
Use realistic operations, runtime endpoints, deployment checks, and user-facing surfaces.
Evidence matters: logs, traces, runbooks, smoke checks, scores, and reviewable artifacts.
Failures are logged, weak ideas are removed, and selected projects move toward release.
Burhuc Labs uses AI-agent security work as product input: prompts can guide behaviour, but important limits need deterministic controls around the runtime, tools, files, credentials, and network.
Bind actions to user, tenant, role, and declared purpose before an agent can call tools or change state.
Restrict commands, package installs, repositories, writable paths, and tool calls outside model instructions.
Block outbound network access unless the destination is explicitly allowed, scoped, and logged.
Keep credentials out of the agent runtime and broker short-lived tokens only when the action requires them.
Technical source input: NVIDIA AI Red Team, Four Ways to Deploy More Secure AI Agents.
CertentIQ records observed behavioural weaknesses. CertentiTrain uses those observations to create controlled remediation candidates for protected re-evaluation.