I've noticed on some of my own research and testing that even with caps some agents will work around the per-call time limit using background processes. Do your caps cover compute in the detached processes?
Would like to hear from everyone: How are you dealing with PRs in 2026? It's our biggest bottle neck.
We are considering looking into attestations, e.g. through the SLSA spec: https://slsa.dev/ to ensure build provenance and verification before it is deployed.
For my own projects: Agents have access to managed tools which enforce quality metrics, build, test, and deploy all more or less in parallel and get it ready to go into git. Examples of metrics are: test coverage, allowed dependencies, linters and the usual. These tools run on the same machine where the software will be deployed (for performance reasons). These tools submit attestations for everything they do. I made my own system for it, it keeps track of everything. The whole process including docker build and deploy to a test env is 10s or less. (I find 10s is reasonable to achieve for all projects I've worked on). Secondly I run a daily process for all projects that runs another set of quality metrics ; picture for example dependencies out of date, tests getting too slow and similar topics. These become work for the agents to pick up. Review is me checking if the app does what it should at best. If it passes all the quality metrics, I don't care much for the code.
For clients (who are not that AI-pilled): They're liking CodeRabbit and developers are not reviewing as strictly anymore. Quality metrics are obtained via CI/CD and attached to PR's. If it's all green then most developers would give it a LGTM
1. Agentic merge queue. Lower risk PRs enter a queue and are auto-merged by a specialized agent. Higher risk PRs are still reviewed by humans. We're currently doing this for Dependabot PRs via a product we built: https://patchwave.ai/ , but the approach generalizes to any kind of PR.
2. Stop doing PRs. Review moves upstream into a multiplayer agent thread that's visible to the team - tag a teammate to pop in and look when you need feedback. PR checks move upstream into this thread as well, the agent just runs them as part of verifying its work. Push to main, ci checks run and deploy as normal from there.
We're at 1 now, but actively working towards moving to 2 and want to help other teams do the same.
If your team is using subscriptions, it is a lot more cost effective (and higher quality) to simply use a local orchestrator to automatically deal with PR reviews. We built https://github.com/codealong/codealong in part to help with this.
Very cool. I was waiting for something like this to pop up.
Is it possible to be added as a data source? I see you're mostly pointing the agent to easy / publicly available sources like the UK.
We have ~200 government registrar integrations now and 500M entities in the system. We essentially have better coverage than OpenCorporates at this stage. https://savvyiq.ai
The core concept is to have a frontier or very smart model run as an agent first for N times. The model get tools, skills etc and instructions. It does the job. At the end it leaves a note (like a retro) explaining what it did and what it could have done better.
As the agent runs, it also writes workflows to do the parts of the job it knows it can do in a more deterministic way (based on previous nodes and thinking traces of other runs etc). We made a dsl for this that has routing, classification, code nodes, etc to build these workflows.
For repetitive tasks (which is our focus) we find that after ~100 cases, the agent most times just runs a workflow end to end instead of looping towards a solution. Workflows are cheap, so the job ends up being way cheaper than an agent in a good model.
Cool project, I myself have a similar autonomous agent, in python
At rinkesh2010rpp/sudarshana. But I liked your mission control. Will for sure take some inspiration.
Also, what I have is ability to find other agents on run time, running on Modal, and a safety layer that checks every actions taken by the agent.
It hasn't gotten better since a year ago, I don't have high hopes. People need to get better at making smaller decisions and having more opinions, not just prompt "Create page for dashboard".
This is fair feedback. We spent a ton of time in the UI for our hosted product and the main website (agentrun.ai) but very little in the OSS console. We’re spending more time on this now and making it better.
We are considering looking into attestations, e.g. through the SLSA spec: https://slsa.dev/ to ensure build provenance and verification before it is deployed.
For clients (who are not that AI-pilled): They're liking CodeRabbit and developers are not reviewing as strictly anymore. Quality metrics are obtained via CI/CD and attached to PR's. If it's all green then most developers would give it a LGTM
1. Agentic merge queue. Lower risk PRs enter a queue and are auto-merged by a specialized agent. Higher risk PRs are still reviewed by humans. We're currently doing this for Dependabot PRs via a product we built: https://patchwave.ai/ , but the approach generalizes to any kind of PR.
2. Stop doing PRs. Review moves upstream into a multiplayer agent thread that's visible to the team - tag a teammate to pop in and look when you need feedback. PR checks move upstream into this thread as well, the agent just runs them as part of verifying its work. Push to main, ci checks run and deploy as normal from there.
We're at 1 now, but actively working towards moving to 2 and want to help other teams do the same.
Is it possible to be added as a data source? I see you're mostly pointing the agent to easy / publicly available sources like the UK.
We have ~200 government registrar integrations now and 500M entities in the system. We essentially have better coverage than OpenCorporates at this stage. https://savvyiq.ai
https://agentrun.ai/docs/self-improvement
https://agentrun.ai/docs/learning-reference
The core concept is to have a frontier or very smart model run as an agent first for N times. The model get tools, skills etc and instructions. It does the job. At the end it leaves a note (like a retro) explaining what it did and what it could have done better.
As the agent runs, it also writes workflows to do the parts of the job it knows it can do in a more deterministic way (based on previous nodes and thinking traces of other runs etc). We made a dsl for this that has routing, classification, code nodes, etc to build these workflows.
For repetitive tasks (which is our focus) we find that after ~100 cases, the agent most times just runs a workflow end to end instead of looping towards a solution. Workflows are cheap, so the job ends up being way cheaper than an agent in a good model.
Also, what I have is ability to find other agents on run time, running on Modal, and a safety layer that checks every actions taken by the agent.
and happy to talk more.
Thanks