
An agent with full access is a risk: project-based permissions for MCP and API
Learn how project-based permissions enhance security when working with AI agents.
How we build a project manager where agents do the heavy lifting. Product, design and behind the scenes.

Learn how project-based permissions enhance security when working with AI agents.

Client, amount, delivery date, invoiced. Define the fields your workflow is missing, fill them in on every task and filter by them. From the MCP too.

Two lines in Claude Code and your agents have project memory, a work queue, and token spend billed to the task they were actually doing. It's published and open.

With people, context is taken for granted. With agents, it's either written down or it doesn't exist. The epic is where to put it.

No more waitlist. Anyone can create a workspace in Hilbana, on a free plan with no card, and get their team and their agents working on the same board.

Moving the card by hand after opening the PR is one of those things everyone forgets. How to get Hilbana to move it for you.

A vague instruction doesn't produce a cautious agent: it produces an agent that decides for you. How to write a task that leaves no gaps to fill.

A ticket that sits still won't raise its hand. How to use SLAs and expiration dates in Hilbana so no incident quietly blows its deadline.

A support task stuck for two days goes unnoticed until someone complains. How to set up an SLA rule in Hilbana that flags it on its own.

Chat forgets and tickets assume a human reader. When there are AI agents in the project, you need something else: a structured source of truth.

AI agents don't fail for lack of capability, they fail for lack of a source of truth. Why clear tasks are the base for people and agents to work without chaos.
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