Hey everyone,
After launching Intrascope and finishing Top 10 Product of the Day, we wanted to open a quick discussion.
The biggest takeaway for us wasn t the ranking, but the conversations. We talked to teams who are already using AI daily and are struggling with scattered tools, separate API keys, lost context, and costs growing without visibility.
That s exactly why we built Intrascope: a shared AI workspace where teams bring their own API keys, work with shared context and Manifests, and keep usage and costs predictable.
Intrascope.app
vladimir, congrats on the relaunch. the "companies want benefits of centralized AI without managing keys" insight is the right read, same pattern we're seeing on the runtime side. we're shipping a circuit breaker for runaway agent loops at iter 3 today too (reserve-then-commit budgeting per action). different layer in the same stack. lmk if u see overlap on the customer side, would happily compare notes.
Intrascope.app
@atul_yadav20 Thanks a lot, really appreciate it.
Yes, I think we are seeing the same shift from different layers. Companies want the value of AI, but they do not want every team, user, or agent operating through unmanaged keys and invisible spend.
Your runtime circuit breaker sounds very relevant, especially as agentic workflows become more common. On our side, we are focused more on the workspace, permissions, project context, model access, and spend visibility layer.
Definitely happy to compare notes. I think there could be overlap on customers that are starting to move from simple AI usage into more governed and automated AI operations.
@intrascopeai the line between "visibility layer" and "runtime guard" is basically the whole product space, both halves are needed and neither is enough alone. would love to compare notes once both products are past launch dust.
the overlap i'm most curious about: when an intrascope customer's per-agent spend visibility surfaces "agent X is burning 4x more than agent Y", does that handoff to a breaker (kill / throttle) or just a slack ping? that's the integration shape that keeps coming up in convos with builders.
happy to dm or do a 20 min call after thursday.
Intrascope.app
@atul_yadav20 I think both layers will naturally converge over time. Today it might start with visibility and alerts, but eventually teams will expect governance to translate into actions when needed. Definitely happy to compare notes once things calm down a bit after launch. Feel free to DM me your calendar link and we'll find a time to chat. We'll definitely loop in @stefan_car as well since he's been heavily involved in shaping this side of the product. Stefan, curious to hear your perspective on this too.
Intrascope.app
Hey @atul_yadav20
From the engineering side, today it is more gateway enforcement than notification / alert.
Because requests go through Intrascope, we can block the next call when limits are hit (model blocks, spend caps, balance checks). But stopping a runaway agent loop mid-flight is a different problem. That is where something like your reserve-then-commit / circuit breaker approach makes a lot of sense.
I see the layers as complementary: Intrascope for centralized access + spend policy at the boundary, runtime guard for per-action control inside agent workflows.
Would be great to explore this topic further!
Mailwarm
To be honest, centralized usage and cost tracking is what most teams end up needing after the first month, so I think this is amazing and very helpful.
Intrascope.app
@thamibenjelloun Thanks! That’s exactly what we’re hearing from teams. The challenge quickly shifts from getting access to AI to managing visibility, governance, and costs at scale.
Wow such a tight race in this market rn
Good luck!
Intrascope.app
@svyat_dvoretski Thanks a lot!
Yes, it’s definitely getting crowded, but we believe the next big problem won’t just be access to AI models.
It will be how teams use AI with proper governance, permissions, cost control and shared context.
That’s the part we’re focused on with Intrascope.
Intrascope.app
For those already using AI across teams, what has been the hardest part: setting spending limits, managing access, or understanding actual usage patterns?