I sat with one of our teams a while back - multiple agents running in parallel, each on a different feature, output that used to take a sprint landing before lunch. I asked mid-session: did that bug from last week get fixed? The one a customer had flagged. Everyone paused. The agent had been asked to fix it. Someone thought it shipped. But had it reached prod? Was it still in staging? It might have gone out in one of the four releases that week and nobody tracked which one carried it. When that much is moving at once, the fix existed somewhere - they just had no way to know where.
They opened four tabs. Checked a CI dashboard. Skimmed Slack looking for someone's deployment message. Twenty minutes gone.
Coding agents move fast - until binaries go live
Coding agents shifted where the bottleneck is. Writing code was the hard part. Now it's everything after: releasing, knowing what actually shipped and understanding what's running in which environment.
When you ship faster, the operational surface grows - more releases, more artifacts, more environments to track. The tooling most teams use was built for a slower world, one where humans managed each step, version numbers were meaningful, and an artifact repo was something your DevOps person configured once and left running.
Here's where that breaks.
Your coding agent has no idea what's actually deployed. "Is the new auth flow live on staging?" "Did the security patch reach prod?" It can't tell you - no access to runtime environments. Instead of you hunting down answers, the agent tries to do the heavy lifting - scraping past commits, reaching into prod if you have the right access, and digging through issues. But because of its limited memory, it only ever sees a fraction of the bigger picture.
Version numbers have also stopped meaning much. "Deploy v2.4.1" is nearly useless as a description of intent. What you actually want is "deploy the latest build that fixed the checkout bug" or "what's different between the version in staging and prod" - but connecting code changes to what shipped to what's currently running is still manual, and it gets harder the faster you move.
There's also the setup cost that nobody names out loud. Distribution management, package manager configs, CI/CD integration - before you've shipped anything, you've burned real time on infrastructure. For teams with no dedicated DevOps person, it's often what quietly kills momentum.
When all of this is still on you - tracking what built, what shipped, what's running where - it doesn't matter that the agent writes code fast. The last mile is still yours.
Fly: Giving Binaries Agentic Wings
We built Fly because we kept running into exactly this. Teams moving fast with AI, then stalling at the release and visibility layer where nothing is agentic and nothing talks to anything else.
The artifact registry needs to be part of the workflow, not a separate system your agent has never heard of. That's where the ten minutes go.
In Fly, every push creates a traceable release with its PR, commits, and change summary attached automatically. That context is available through MCP, so Cursor, Claude Code, Copilot - whatever you use - actually knows what's been built and what's deployed.
You can ask "find the release that fixed the checkout bug" or "what changes are queued between prod and staging?" or "deploy John's latest changes to production" and get something useful back instead of a blank stare or a three-minute token flood that may or may not give the right answer. You can ask what’s running in any environment without leaving your coding session, and get answers from up-to-date semantic tracking across every runtime, fully agentless.
Releases are identified by changes they contain, not just by a version number someone incremented in a pipeline. Setup is a few minutes: connect GitHub, run the Fly bash command, push once, and it picks up context from there.
You can even ask Fly to slack you back "when Rachel pushed the new UI design to staging".
We've been running this with teams for several months. The thing I hear most often is: "I didn’t realize how much time this was costing us until we had a better way." That's the right outcome - when the tooling becomes invisible in your workflow.
If your team is shipping fast, but figuring out where finished code is actually running still costs you valuable minutes and multiple DMs, it’s time for a better way.
Try this: jfrog.com/fly
Disclosure: I’m AI Lead at JFrog. I use Fly day-to-day.
