Fragmented systems
Claude, Codex, Cursor, OpenClaw, Hermes and future agents cannot reliably discover, call, or supervise one another.
One control plane. Every agent.
AI agents are becoming powerful, specialized, and fragmented. AI Agent PM aims to connect them into reliable loops, routing each task to the right model, tools, context, and execution environment.
1 human operator
10s, 100s or 1,000s of AI agents
Early-stage project. Architecture and first integrations in development.
Agent fabric online
Launch market intelligence workflow
5 agents · 3 providers · completion criteria active
Search and collect evidence
Research agent · low-cost model
Analyze the opportunity
Reasoning agent · private context
Build the automation
OpenAI coding agent · repository tools
Verify completion
Review agent · tests and criteria
Human input requested
Approve access to the production analytics workspace.
The problem
More agents do not automatically mean more useful work.
Today, every agent lives in its own interface, carries its own tools and context, and consumes a different provider allowance. Humans are left stitching the work together and watching every step.
Manual orchestration
Tasks, context, outputs and approvals are copied between chats instead of flowing through durable loops.
Wrong model, wrong task
Frontier models are wasted on routine work while specialized, cheaper or more secure agents sit unused.
The vision
Manage work by exception.
Define the trigger, the agents allowed to participate, the tools they can use, and what “done” means. Then let the loop run.
The long-term goal is to let one person supervise not only a handful of assistants, but swarms of hundreds or thousands of specialized agents across projects, providers, machines, and execution environments.
The human should intervene only when an agent is stalled, stopped, blocked, or waiting for a decision.
1
Explicit agent permissions
Choose exactly which agents and tools each agent may call.
2
Triggers and reusable loops
Start work manually, on a schedule, through cron, webhooks, or events.
3
Visible work and intervention
See completed, current and upcoming tasks, and exactly why work has stopped.
4
Evaluation until completion
Agents review their results against explicit criteria, correct failures, and keep going within limits.
Agent work, connected
From isolated prompts to operating loops.
The same control layer can coordinate specialized agents across the work that founders, operators, analysts and teams already perform.
Automated search and research
Scout the web, papers, repositories and data sources; collect evidence; compare methods; produce traceable conclusions.
Coding and software delivery
Delegate implementation to coding agents, run tests, request reviews, repair failures and continue until acceptance criteria pass.
Marketing and distribution
Monitor markets, discover timely angles, generate channel-specific assets, review quality and route approved work for publication.
Financial and business operations
Collect structured data, update analyses, investigate exceptions, generate decision-ready outputs and flag items requiring human review.
Built to evolve
The agent stack changes every week. The system should learn with it.
AI Agent PM is intended to continuously scout new agents, papers, skills, loop designs and orchestration methods, then evaluate them before integrating proven improvements.
Discover
Evaluate
Experiment
Integrate
Improve
AI Agent PM
A simpler way to command agent swarms at any scale.
One place to configure agents, connect them into loops, supervise hundreds or thousands of workers, and step in only when human judgment is genuinely required.
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