Teams should let people reach the same agent session from Slack, the product app, GitHub, and other places where work happens.
2
Meeting bots and other integrations can turn customer conversations, bugs, and internal discussions into concrete code changes without someone manually copying requests between systems.
3
Cloud sandboxes limit agent access, protect against accidental data loss or exfiltration, and let non-technical staff trigger real engineering work.
Summary
Arjun Singh describes how Superconductor integrates coding agents into the team's existing human workflows. A single agent session can move between Slack, the app, GitHub, and other interfaces while keeping its context, making the work visible to everyone involved. Their meeting bot listens to customer calls, onboarding sessions, and team meetings, then turns useful ideas into tickets, prototypes, and sometimes pull requests. Singh argues that this only works safely when agents run in isolated cloud environments with restricted credentials and network access. The same setup lets support and growth staff ask agents to fix bugs without a local development environment or a long handoff through a ticketing process. Superconductor also benchmarks agents on its own Ruby on Rails codebase, comparing quality, cost, and time instead of relying only on public benchmarks. The team still has humans review every pull request, while switching between models and harnesses as their measurements change.
Teams should stay independent of any single model or harness
Singh says the best model or harness can change every week because a new option appears or an existing one becomes unavailable. Open-weight models have also become good enough for Superconductor to explore, and they cost less in some cases. Staying flexible keeps a change in vendor or model from disrupting the whole team's workflow. It also keeps spending under control because token sellers benefit from more usage, while the team wants to pay only for the work it needs. Superconductor wants to switch tools without forcing engineers to learn a new workflow each time.
One agent session should follow the work across every relevant interface
A coding agent trapped on one developer's laptop cannot be reached by teammates. Slack bots improve access, but Singh says they still trap the session inside Slack. Superconductor instead keeps the same session available in Slack, its app, desktop or mobile environments, GitHub, and other interfaces. Someone can begin a task in Slack, continue it in an engineering-focused app, and finish it in GitHub without losing context. Reviewers can ask the agent why it made a choice inside the existing thread instead of waiting for the original author to see a notification and reply.
Visible agent work gives non-technical teammates a way to review it
The team makes sessions collaborative by showing who interacted with them, who has been notified, and who has seen the work. This matters when a support or growth employee creates a ticket and an engineer needs to know whether it has been reviewed. Singh says agents can also attach artifacts such as screenshots and videos wherever the work started or ended. A reviewer can inspect those artifacts without searching through separate systems. The session becomes a shared piece of work rather than a private process running on one person's machine.
External conversations can become code without a manual handoff
Singh groups Slack conversations, customer meetings, onboarding calls, sales calls, team meetings, bug reports, emails, and feature requests under external signals. Connecting an agent to these systems through MCPs still leaves a person deciding which item to hand over. Superconductor instead ingests signals, prioritizes them, and acts on them. Its meeting bot can join a Google Meet, Zoom, or Teams call and listen for useful work. During a four-hour expo meeting, it noticed a request for clearer acceptance criteria, opened a ticket, started implementing it, and added two fields to the team's ticket form. Singh says the result still needs judgment, but it gives the team a concrete prototype to evaluate.
Cloud isolation matters more for access control than for keeping a laptop open
Running agents in an isolated cloud environment lets work continue when a developer closes a laptop. Singh says that convenience was an early reason his team moved to the cloud, especially when he had a young child, but he considers least privilege more important. Developer laptops may contain credentials and files that an agent should not see. An agent told to wipe a staging database could find a token on the laptop, mistake it for staging access, and delete production data. Isolation narrows what the agent can reach and gives the team more freedom to run experiments.
Network sandboxes should control both access and exfiltration
The team also restricts where an agent can send or retrieve data. A configurable network sandbox lists allowed and blocked destinations, then asks for approval when the agent tries to reach a new one. Singh gives vendor documentation as an example of access that might be granted when needed. The restriction applies to code, projects, secrets, and other content, so the agent cannot freely exfiltrate them. Access can be granted for one ticket or for an entire project. Singh connects this setup directly to letting support and growth staff trigger fixes from Slack without giving agents broad access to company systems.
Benchmark agents on representative work from your own repository
Superconductor selects pull requests that represent good engineering work, whether they were created by a person, an agent, or both. The team runs chosen agents against those tasks and measures quality against cost and time on its own codebase. Singh says public benchmarks can mislead because their tasks may not resemble a team's work. SWE-bench uses Python tasks, while Superconductor works in Ruby on Rails. Their internal results showed different tradeoffs among Anthropic agents, Codex, Cursor, and open models. The team changed its default to Codex after its measurements matched their informal impressions, then switched briefly when another tool appeared and later disappeared.
Singh says 99.9% of Superconductor's pull requests are heavily agent-generated, but every pull request is reviewed by a human because quality, reliability, and security matter. Over the past month, the team used 1.5 billion tokens and ran 3,300 Claude Code sessions with about $10,000 in token value under its plan. Codex ran four times as many sessions and cost less overall, so most current work is merged through Codex. The team still uses other models, including GLM 5.2, and plans to use its repository benchmarks to route different tasks to the models that work best for them.
"And so it takes your hundreds or thousands of ideas that are everywhere and it helps you kind of move with the speed of what your customers are asking you for and what they're thinking."08:10
Who should watch
You are building a team workflow where coding agents currently live on individual laptops and teammates cannot easily inspect or continue their sessions.
Your product ideas arrive through meetings, support conversations, and customer calls, but someone still has to copy each request into a development system.
You want agents to run more autonomously while limiting credentials, network access, and the chance of touching production data.
