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Superflux · Agent Mission Control

Mission Control for Production Agents.

Superflux helps teams ship real agents — API or conversational, with UI and deployment — then operate them with traces, approvals, evals, and harness-level debugging.

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run · agt_8f2a · claude-agent-sdk ·live
Run timeline
agent_run.trace
  1. 00:00.412
    plan() · decompose user request
  2. 00:01.108
    skill: research.web_search
    v3.2
  3. 00:02.940
    tool: github.search_repos
    200 OK
  4. 00:04.221
    tool: drive.list_files
    ETIMEDOUT · retry 1/3
  5. 00:06.880
    tool: drive.list_files
    unreachable · fallback skill
  6. 00:08.014
    skill: research.cache_lookup
    hit · stale 14d
  7. 00:09.530
    destructive: file.write report.md
    policy: approved
  8. 00:11.002
    destructive: deploy.push prod
    policy: blocked
  9. 00:12.610
    handoff → human review
    slack #agent-ops
Skills used
  • research.web_search
    ×4
  • research.cache_lookup
    ×2
  • github.search_repos
    ×3
  • drive.list_files
    ×2
  • file.write
    ×1
Tool availability
  • GitHub
    reachable
  • Google Drive
    unreachable
  • Browser
    blocked by policy
  • Postgres
    access denied
  • Slack
    approved
Context & memory
Context window72% · pressure
Memory recall hit-rate48% · stale
Skill coverage86%
Detected patterns
  • repeated retry · drive.list_files
  • unreachable dependency · 2 runs
  • package workflow → skill suggested

A real production agent run — user request, plan, tool calls, an approval, a blocked destructive action, a failed dependency, and a recommendation to package the repeated workflow as a skill.

The problem

Agent demos are easy.
Production agents are hard.

Once an agent touches real tools, files, customer data, approvals, or business workflows, the team needs to know what actually happened. Not a chat log. Not a vibe. A trace they can stand behind.

Superflux is built for the operational reality that starts the moment an agent leaves the demo: what the agent did, which tools it touched, what failed, which actions were risky, what needed approval, why an answer was wrong, and what should be hardened before the next run.

what the agent didtrace
which tools it usedaudit
what faileddebug
what was riskypolicy
what needed approvalgate
why the output was wrongreplay
what context was stalehealth
what to harden nextskill
Who it's for

Built for teams moving agents from demo to production.

Superflux is for teams that want to ship real internal or customer-facing agents without building the full harness, UI, deployment, tracing, approval, and debugging layer themselves.

Technical founders

Shipping the first real agent your customers or team will rely on.

CTOs & engineering leaders

Standing up the operating layer before agents touch production systems.

AI & product teams

Moving from prototype notebooks to a deployed agent with a real UI and real users.

Enterprise innovation teams

Proving an agent works inside the org without building the harness from scratch.

AI platform teams

Standardizing how every agent across the company is traced, governed, and improved.

What Superflux does today

Ship production agents without building the whole harness yourself.

Teams come to Superflux when the prototype is working but the path to production looks like six months of plumbing. We build, deploy, and operate the agent — UI, tools, approvals, and all — alongside your team.

Available today

API agents

Production-grade endpoints your product or backend can call.

Conversational agents

Chat surfaces wired to real tools, memory, and approvals.

Custom agent UIs

Bespoke front-ends for the agent your team actually needs.

Dedicated Vercel subdomains

Shippable URLs for internal pilots or customer-facing rollouts.

Tool-integrated workflows

Wired into the systems your team and customers already use.

Human approval flows

Risky actions routed to the right operator before they execute.

Production deployments

Environments, secrets, and rollouts handled — not improvised.

Iteration on real workflows

Refined against actual customer behavior, not synthetic demos.

The Mission Control layer

The operating surface around production agents.

Superflux ships your agent today. Mission Control is the layer designed to operate and improve it as it becomes a real system. Some surfaces are available today, others are evolving as we deploy with customers, and a few are the platform direction we're building toward.

01today

Run timeline

Every step of every run — plan, tool call, retry, handoff, output — with timestamps and outcomes. Replayable end-to-end.

02today

Tool calls

Which tools the agent called, with what arguments, and what came back. Reachability, latency, and failure surfaced inline.

03today

Skills used

Which skills the agent reached for, how often, and where they succeeded or fell back. Versioned and correlated to behavior.

04today

Approvals

Risky actions routed to the right operator, with the policy that triggered the gate and who signed off captured as a first-class event.

05today

Blocked actions

File writes, deletes, deploys, external sends — every destructive action shown with the policy that allowed or blocked it.

06today

Failed dependencies

Know whether the agent failed or its tool did. Reachability and policy state captured per run, not guessed afterwards.

07evolving

Repeated failure patterns

Retries, bad handoffs, missing sources, stale context — surfaced across runs, not buried one trace at a time.

08evolving

Memory & context health

Context window pressure, recall hit-rate, stale or missing context. Flags when the harness is operating on degraded inputs.

09evolving

Eval results

Runs scored against the rubrics your team cares about, so regressions show up before customers report them.

10direction

Skill & workflow recommendations

Repeated debugging becomes a candidate skill. Repeated tool failures become candidate validators. Every run teaches the harness.

How Mission Control plugs in

Three ways to put Mission Control around your agents.

Mission Control is the platform. Harness adapters and hooks are how it connects. Skills are reusable workflows the platform can track, recommend, and improve. Sandboxes are execution environments — not harnesses.

01Mode

Managed by Superflux

We build, deploy, and operate the agent for you — harness, UI, tools, approvals, and the Mission Control layer around it.

Available today
02Mode

Bring your harness

Connect Claude Agent SDK, Google ADK, or a custom harness through adapters and hooks. Runs, tool calls, and approvals flow into Mission Control.

Claude & ADK today · others planned
03Mode

Run in your environment

Deploy through Vercel, GKE Agent Sandbox, or Sandboxed Google Cloud Run. Mission Control observes and operates across them.

Deployment-flexible
Mission Controlthe platform
Adapters / hookshow it connects
Skillsreusable workflows
Sandboxesexecution, not harness
Ecosystem

One control plane.
Every layer of the agent stack.

Harnesses, managed agents, and execution environments are different things. Mission Control treats them that way — so the same traces, approvals, and patterns apply across all of them.

Harnesses / frameworks
  • Claude Agent SDK
    today
  • Google ADK
    today
  • OpenAI Agents SDK
    planned
  • LangGraph
    planned
  • Custom harnesses
    planned
Managed agent options
  • Claude managed agents
    supported
  • Superflux managed agents
    supported
Execution / isolation
  • Vercel deployments
    supported
  • GKE Agent Sandbox
    supported
  • Sandboxed Google Cloud Run
    supported
The improvement loop

Every production run teaches the harness what to harden next.

Mission Control is designed to be more than observability. The patterns it surfaces across runs feed directly back into the harness — as skills, validators, policies, and memory strategies.

Repeated debugging workflow
Package into a skill
Repeated tool failure
Harden the connector
Repeated human correction
Add a validator
Repeated risky action
Add an approval policy
Repeated context overload
Improve memory & context strategy
Use cases

Built for the agents teams actually run.

Every use case has the same operational need: know what happened, catch what failed, approve what's risky, and harden what repeats.

Coding agents

Inspect file writes, tool calls, failed tests, blocked deploys, and the repeated debugging loops worth packaging into a skill.

Research agents

Trace sources, approvals, tool calls, and failed retrievals so teams can trust the final brief.

Compliance agents

Show evidence, approvals, and blocked actions before any response leaves the system. Replay decisions on demand.

Internal operations agents

Track tool reachability across the org and spot repeated bad handoffs between agent and operator.

Customer support agents

Diagnose why an answer was wrong — missing memory, stale context, blocked tool, or harness failure — not just that it was.

Data analysis agents

Monitor token pressure and context health on long runs. Detect when results stop being trustworthy.

Long-running workflows

Audit multi-step runs end to end: every handoff, retry, approval, and recovery captured as first-class events.

Final word

Build agents fast.
Operate them with control.

Superflux helps teams ship production agents today, and is building the Mission Control layer for operating and improving them as they become real systems.

Book a demoSee a production run