Prove one consequential agent workflow can be governed before it acts.
Ruligent is inviting a small number of teams to run one real AI-agent control boundary for 30–45 days. The goal is not a generic beta or a logo wall. The goal is evidence: what the agent attempted, which policy applied, whether a human approval was required, whether live controls changed before execution, and what can be reconstructed afterward.
No-fee design partnership while new paid enrollment remains closed. Participation does not guarantee future commercial terms, launch timing, outcomes, or acceptance into the cohort.
Teams already giving agents real tools and real authority.
The strongest design partners are not experimenting with chatbots. They already have agents that can change external state and need a defensible control point before execution.
Browser and computer-use agents
Agents that can click, submit forms, change settings, purchase, publish, or otherwise act through a browser.
Tool and MCP platforms
Agent systems that expose powerful APIs or tools and need policy, approval, spend, or kill-switch enforcement across calls.
Enterprise agent workflows
Internal agents touching customer communications, production systems, sensitive data, money, deployments, or regulated processes.
One narrow workflow, instrumented end to end.
We deliberately avoid broad “govern all your agents” pilots. A founding partnership should produce a control proof that can be explained, repeated, and audited.
Choose the action
Define one consequential tool call or workflow that already matters in production, staging, or a realistic pilot environment.
Define authority
Map what may be allowed, blocked, rate-limited, spend-limited, or forced through human approval.
Exercise live controls
Test approval, stale-approval rechecks, policy changes, spend limits, and kill switches against representative execution attempts.
Capture evidence
Review decision events, reasons, approval identity, outcome evidence, latency, failures, and the exact control gaps discovered.
A partner should leave with evidence, not a promise.
Control evidence
- Representative allow, block, and approval-required decisions
- Proof that a live kill switch or stricter policy overrides stale permission
- Measured latency and failure behavior for the chosen workflow
- Evidence that risky execution remains human-controlled where required
Operating evidence
- A documented integration pattern the partner can repeat
- Security and procurement objections surfaced against a real environment
- Known implementation gaps ranked by production risk
- A clear go, no-go, or continue-to-harden decision at the end of the engagement
Useful proof without forcing unsafe disclosure.
Private by default
Partner workflows, credentials, policies, implementation details, and proprietary data do not become public marketing material merely because a design partnership exists.
No automatic endorsement
Participation is not represented as an endorsement, certification, safety guarantee, or customer relationship unless both sides separately approve that statement.
Case-study permission is separate
An anonymized or named case study is optional and requires separate permission. The engagement remains useful even if nothing is published.
Concrete is better than “AI governance.”
Browser action
An agent attempts a high-impact browser action. Ruligent evaluates current policy immediately before execution, requires approval when needed, and records the decision and evidence.
External communication
An agent prepares an outbound message but cannot send until the governing policy and required human approval are satisfied at execution time.
Money or production change
An agent requests a spend or deployment action. Current spend limits, risk rules, approval state, and kill-switch status determine whether execution proceeds.
Bring one consequential workflow.
If the control boundary matters, we would rather prove it on one real action than pitch you fifty slides.