Most teams ship agents before they have any governance in place. This is the specific failure mode that creates the need for Blekline.
The incident that happens to every team
An agent in Cursor is asked to set up a deployment pipeline. It reads the repo, finds a .env file with a database connection string, and passes it — verbatim — into a model API call as context. The model processes it. The task completes. Nobody notices.
Except now that connection string is in a model provider's request log. It may be in the agent's conversation history. If the workspace is shared, other users can see it. There was no alert. No block. No audit record. The agent did exactly what it was designed to do: complete the task.
This is not a model problem. Models don't know your data classification policy. This is not a prompt engineering problem. You cannot reliably instruct an agent to "never include secrets" across every task, every tool, every context window.
This is an infrastructure problem. And it requires an infrastructure answer.
What's been missing
Every MCP call between an agent and a tool — or between an agent and a model API — was ungoverned. No interception. No policy check. No record beyond whatever the client UI decided to store.
Blekline sits at that boundary. Every call goes through it before reaching a model or a Daytona sandbox. It masks, evaluates, and emits an audit event. The agent doesn't change. The tools don't change. The governance is structural.
Why this matters for the EU AI Act
The EU AI Act's human oversight requirements (Article 14) don't mean a human watches every agent call. They mean your system must be capable of intervention — tool calls can be flagged, blocked, or reviewed. The audit trail requirements (Articles 12 and 17) mean you need tamper-evident records of what your AI systems did and why.
Blekline provides both. Tool call enforcement is the intervention mechanism. The event stream is the record. The mask decision on every prompt call is the data governance proof.
GPAI obligations are enforceable now. Full high-risk and transparency requirements land in August 2026. If you're building on AI today, you're building the compliance surface for tomorrow.
See EU AI Act & compliance for the full regulation mapping.
Why not prompt engineering or model guardrails
Prompt-level instructions are best-effort. Models follow them until context grows, until a different model is selected, until an agent chains multiple calls and loses the instruction. They are not enforceable.
Model providers' built-in guardrails catch harmful outputs — not policy violations specific to your organization. They don't know your tool allowlist, your PII classification, or your data residency requirements.
Ingress governance is enforced at the call level, before the model sees it, before the tool executes. It doesn't depend on model behavior. It's the same regardless of which model your agent uses.
Next steps: Quick start · EU AI Act mapping · Architecture · MCP proxy