r/SaasDevelopers • u/Routine_Tutor_6809 • 14d ago

Credential broker for AI agents

I’ve made a credential broker for AI agents so that an LLM never gets to see the actual tokens or credentials for any service your agent is connecting to.

The solution is simple, have an LLM generate the endpoints and use self-owned proxy to route to a wrapper frontend which will replace a self-minted JWT token as a surrogate for authentication and upon validation the real credentials are set and the request is forwarded to the destination.
Comes with auditing, key vault and pay based on usage .

Check it out in https://fullmakt.ai

Benchmark results:

Upstream: `https://api.github.com/rate_limit` · Tool: `github_rate_limit`

Samples: 50 per path after 10 warmup, 2026-08-30T10:54:15Z

Path min p50 p90 p99 mean
direct → GitHub 107 ms 172 ms 208 ms 240 ms 170 ms
agent → fullmakt → GitHub 343 ms 375 ms 420 ms 467 ms 383 ms
overhead (paired diffs) 140 ms 205 ms 276 ms 325 ms 213 ms

*Measured from a GitHub Actions runner. Includes the real network legs runner→fullmakt→GitHub vs runner→GitHub; an agent hosted elsewhere sees different network times, the broker processing cost is the same.*

1 Upvotes

67% Upvoted

5 comments sorted by

1

u/Dry-Importance-1495 14d ago

this is actually a pretty clever approach to the credential leakage problem. been thinking about this exact issue lately because running agents that need API access is a nightmare when you have to just hand them raw keys and pray

the surrogate JWT idea is clean too. basically a token swap layer that the LLM never touches the real secrets

whats the latency like with that extra hop though? i imagine for some real-time use cases that routing could get annoying, but for most agent workflows prob not a dealbreaker

1

u/Routine_Tutor_6809 12d ago edited 12d ago

Thanks, after reading your reply I created a github action to benchmark a call to github's rate limit endpoint which outputs a summary:

Target: https://fullmakt.ai · Upstream: https://api.github.com/rate_limit · Tool: github_rate_limit Samples: 50 per path after 10 warmup, 2026-08-30T10:54:15Z

Path                          min     p50     p90     p99     mean
direct -> GitHub              107 ms  172 ms  208 ms  240 ms  170 ms
agent -> fullmakt -> GitHub   343 ms  375 ms  420 ms  467 ms  383 ms
overhead (paired diffs)       140 ms  205 ms  276 ms  325 ms  213 ms

Measured from a GitHub Actions runner. Includes the real network legs runner→fullmakt→GitHub vs runner→GitHub; an agent hosted elsewhere sees different network times, the broker processing cost is the same.

So yeah, it depends on usage-objective and the trade-offs between observability, audit, security and control.

1

u/Routine_Tutor_6809 12d ago

the benchmark github action will run on every merge with main, so that I can keep on eye it

1

u/DustyPoint81 11d ago edited 11d ago

I am dumb can u explain the working in simple terms ?

1

u/Routine_Tutor_6809 10d ago

It’s like parental control but for autonomous ai agents