Thanks for supporting DCP. I’d love your feedback
Hey everyone, thank you to everyone who followed, upvoted, commented on, or checked out DCP this week.
DCP is a control layer for AI agents.
It helps you give an agent access to the things it needs — tools, API keys, wallets, credentials, or private data — without hardcoding secrets into the agent, dropping them into config files, or exposing them in model context.
You connect your agent to DCP, choose what it can access, and let it work within those permissions.
For a single agent, DCP gives you safer access, logs, limits, and revoke controls.
If you use multiple agents, DCP becomes one place to manage permissions, activity, and sensitive access across all of them.
The goal is simple: make agents safe enough to do real work.
Not by forcing a human to approve every tiny step, but by giving agents clear permissions, rules, limits, and access controls from the start.
I’d love honest feedback from anyone who tried DCP or looked at the launch:
Was the product clear?
What felt useful?
What felt confusing?
What would make you trust it more?
What should we improve first?
Which agents, tools, credentials, or workflows should we support next?
I’m especially interested in feedback from people building or using AI agents, MCP tools, devtools, wallets, automations, or agent commerce workflows.
Thanks again for the support. Critical feedback is more useful than compliments right now — I’d love to know what would make DCP actually useful in your workflow.
Replies
mailX by mailwarm
It was a great launch! but what would make it more useful for me is tighter support for common agent stacks like MCP-based tools, wallet interactions, and API-heavy workflows where access changes often. Also stronger defaults for “safe first setup” would help reduce the friction of getting started in real projects
DCP
@daniel_nwankwo Thanks Daniel — really appreciate it.
That’s exactly the direction DCP is built for: MCP-based agents, wallet actions, API credential access, scoped permissions, budgets, approvals, and remote agents are already supported today.
Your point on making this easier to use in real projects is very fair though. I think we can do a better job showing the common workflows clearly, especially during the first setup.
We already have a one-command setup for Claude, Cursor, OpenClaw, and Hermes. Curious which agent stack would you want to connect to DCP first in your own workflow?
The product is clear to me, but I’d make the “permission shape” even more visible for first-time users.
The scary part with agent access isn’t only secret storage; it’s understanding what the agent can actually do once access is granted. A starter view that says “this agent can read X, write Y, spend/execute Z, expires on this condition, and never sees the raw key” would make DCP easier to trust.
For teams, I’d also want a few common policy presets — research-only, draft-only, customer-data read-only, deploy with approval — so the first setup starts from safe boundaries instead of a blank permissions model.
DCP
@jim_jeffers This is really helpful, Jim! thank you.
DCP already starts from that model: when an agent connects, it has no permissions by default. The user then grants only the access that the agent needs.
But you’re right that the permission shape should be much more visible and easier to understand at a glance.
I’ll work on making that clearer in onboarding and the dashboard, and I’ll also add more common presets so users can start from safe defaults faster.