Agent registry
Define roles, scopes, tools, memory access, default models, and handoff rules.
AI project command center
Agent Manager
Manage the models, agents, tools, approvals, and human decisions behind every AI-assisted project from one calm operating layer.
Model-neutral
Human-gated
Project-aware
One project brain
Specs, context, tasks, decisions, and agent history stay together.
Many AI workers
Coordinate coding, research, design, review, QA, and ops agents.
Clear control
Route by cost, privacy, capability, urgency, and approval rules.
Shipping memory
Turn every run into reusable playbooks for the next build.
The category
The next bottleneck is not finding an AI agent. It is managing the work they do.
Teams are already juggling editors, terminal agents, browser agents, research tools, code reviewers, image models, voice systems, and custom automations. Agent Manager gives that stack a shared project layer: who is doing what, with which model, under which rules, and what still needs a human yes.
12
roles mapped
4x
parallel tracks
100%
human-gated changes
Prototype switchboard
Pick the mission. Agent Manager assembles the right operating plan.
Workspace profile
Founder building an MVP
Balanced
Balanced routing
Use premium reasoning for planning, fast models for drafts, and human review before deploy.
Live plan
Launch a validated product prototype
Human gates
3 approvals
Operator triage
Find the first agent ops leak before the stack gets louder.
The best audit starts with one named failure mode. Pick the places where AI work already feels expensive, risky, or hard to hand off.
First audit lane
Context custody
Start by mapping where briefs, repo state, customer notes, and accepted decisions leave the shared project memory.
- Name the source of truth for every agent.
- Decide which context can be reused.
- Set a handoff note format before the next run.
Bring your tools
A neutral layer above editors, agents, models, and deployment systems.
Agent Manager is designed as the place where Cursor-style coding agents, terminal agents, browser agents, review agents, research assistants, and custom internal bots can all share project context without forcing a team into one model or one interface.
Inputs
Specs, briefs, tickets, docs, repos, customer notes
Agents
Plan, code, research, design, test, review, deploy
Models
Reasoning, speed, vision, speech, image, local, private
Controls
Budget, permissions, privacy, evals, approvals, logs
Product surface
Everything a serious AI build needs after the prompt.
Model router
Route each task by reasoning depth, cost ceiling, latency, privacy, and modality.
Approval queue
Pause risky changes for product, security, legal, design, or owner review.
Project memory
Keep goals, constraints, decisions, accepted outputs, and rejected paths in one place.
Cost cockpit
See spend by model, project, agent, environment, and customer-facing workflow.
Delivery gates
Connect tests, previews, changelogs, rollbacks, and launch readiness to agent work.
Governance that does not slow builders down
Let agents move fast inside boundaries the team can trust.
Scope every agent
File access, tool permissions, secrets, environments, and allowed actions are explicit.
Watch every run
Logs, diffs, model calls, cost, artifacts, and decisions become traceable records.
Evaluate outcomes
Agent Manager remembers what worked, what failed, and which playbooks should repeat.
Launch shape
Start as the dashboard for AI-native builders. Grow into the operating system for agentic work.
Now
Project dashboard
Manual agent assignment, project memory, approval queue, and cost tracking.
Next
Tool connectors
Sync with repos, issue trackers, docs, chat, previews, and AI coding tools.
Later
Autonomous orchestration
Policy-aware routing, eval loops, agent marketplaces, and reusable build playbooks.
Paid pilot map
Make the first 30 days specific enough for a buyer to approve.
Agent Manager can earn through a focused operating pilot before it sells a broad platform promise. Pick the buyer motion and package the first month around one measurable workflow, one human approval gate, and one handoff that can become the product.
Founder wedge
30-day MVP command center
Turn one founder's scattered agent work into a visible product sprint with named agents, acceptance gates, cost tracking, and a launch-ready weekly review.
Week 1
Inventory current tools, repo, docs, and recurring founder decisions.
Go/no-go gate
Approve one build lane that can ship with human review.
- Build a shared project memory and agent roster.
- Create approval gates for copy, code, QA, and deploys.
- Deliver a weekly founder control room snapshot.
Paid first step
Name the first safe agent workflow before the full audit.
A lighter entry point for founders and small teams who are using several AI tools but do not yet know which workflow deserves a full Agent Ops Audit or pilot build. Ethan can review the current stack manually, keep credentials out of scope, and return a practical next-step recommendation.
Agent Stack Intake Brief
$150
A focused remote brief that turns scattered AI usage into a clear first workflow: current tools, agent roles, human review, access boundaries, and whether the next move is manual ops, the $750 audit, or a 30-day pilot.
- Inventory the AI tools, repos, docs, automations, and recurring agent tasks already in use.
- Choose one workflow that can be mapped without secrets or production credentials.
- Identify the human review owner, handoff format, and first approval gate.
- Receive a concise recommendation: stop, stay manual, book the audit, or scope a pilot.
No secrets
One workflow
Clear next step
First paid offer
Before building the platform, sell the operating map.
Agent Manager can earn before the full SaaS exists by helping founders and teams inventory their current AI-agent workflow, risk points, approval gates, and tool sprawl. The first pass is intentionally safe: no production credentials, no secrets in the form, and one workflow mapped before any larger build is proposed.
Agent Ops Audit
$750
No secrets
One workflow
Scope reply
A focused founder review that turns scattered AI usage into a practical control plan: agents, tools, permissions, model choices, handoffs, and first automation targets.
- One-page agent inventory and risk map.
- Recommended approval gates for coding, research, content, and deploys.
- Lean workspace architecture for the first Agent Manager prototype.
- Next-week operating plan with owners, rules, and one workflow to pilot.
Start with screenshots, notes, tool names, and decision examples.
Pick one recurring agent process before mapping the whole company.
Ethan confirms fit, access boundaries, and the first audit lane before invoice.
Reserve the first workspace
Build the place where all your agents report in.
Tell us what you want Agent Manager to coordinate first: a product build, an agency workflow, internal automation, or a full AI operating layer for your team.