Hundreds of agents. One window. Run an AI organization at executive scale.
Like managing 20 teams without managing any of them directly. The complexity of each team is abstracted away.
Be the executive. Not the manager.
Big three AI labs collaborating on agent infrastructure standards. The orchestration layer is becoming critical.
Agentic AI Foundation formed under Linux Foundation. MCP, Goose, and AGENTS.md contributed as open standards.
2026 positioned as breakout year for enterprise agents. Focus shifts from experimentation to production.
AI agent startup achieves unicorn status at Series A. Market validates agent infrastructure investment.
Focus shifting to custom models, fine tuning, evals, observability, orchestration, and data sovereignty.
Platform convergence bringing order to agent chaos. Multi-provider orchestration becoming standard.
75% building agentic architectures alone will fail. Orchestration platforms essential for success.
Up from less than 5% in 2025. Agentic AI could drive $450B in enterprise software revenue by 2035.
Research shows multi-agent systems need orchestration, not scale. Tool-heavy tasks see 2-6x penalty without coordination.
Agent sprawl increasing across languages, frameworks, infrastructure. Unified orchestration platforms becoming essential.
Escalating costs, unclear business value, inadequate risk controls. Orchestration gap is killing projects.
Control planes making agents first-class citizens. Infrastructure is the missing piece for enterprise deployment.
The paradox of agents: usefulness requires giving away control. Governance and orchestration are the answer.
Prompts as source code, MCP, AI-native IDEs. Agent-driven workflows shift the source of truth upstream.
Architecture validated through daily production use. Subsystems operational. Productivity gains confirmed.
Final integration layer in progress. Bringing proven subsystems together into cohesive UX.
OpenAI, Anthropic, DeepSeek, Ollama, DeepInfra integration tested. Provider-agnostic architecture stable.
AI-native project planning system complete. fest CLI operational.
Context preserved across sessions. Long-running work validated over weeks of use.
Core patterns extracted from production use. Ready for final integration.
Hundreds of agents. One window. Run an AI organization at executive scale.
Like managing 20 teams without managing any of them directly. The complexity of each team is abstracted away.
Be the executive. Not the manager.
The industry is racing to build smarter models. That’s not the problem.
We already have AI that can do most human tasks. What we don’t have is a way to direct it at scale.
Obey is the executive layer that’s missing.
Like a VP with 20+ teams. You manage the organization, not the individuals.
Each team’s complexity is abstracted away. You think at the level that matters.
Executive control. Not micromanagement.
Run many campaigns in parallel. Each campaign runs many projects. Each project runs agents.
10 campaigns × 10 projects × 2 agents = 200 agents. One window.
It scales to your vision.
Plan every micro decision without actually planning them. The system handles decomposition, quality gates, and execution tracking.
You make high-level decisions. Obey handles the rest.
Abstraction that lets you manage at scale.
TUI first. Then mobile. iPhone, iPad, Android, desktop.
Connect to Obey daemon from any device. Campaigns across machines as if they’re local.
Manage your AI organization from anywhere.
For investor inquiries, early access, or partnership opportunities.
Infrastructure for autonomous companies.
Run an AI organization at executive scale.
Imagine you’re a VP with 20+ teams of 5-10 people each. That’s 100-200 people. You can’t manage them all directly. You need abstraction.
Obey lets you manage hundreds of agents the same way. Not by tracking each one, but by managing at the right level of abstraction.
Campaigns
A campaign is a workspace for a unified initiative. Related projects that share context. One campaign might have dozens of projects building in parallel.
Sessions
Each session can have multiple agents working together. Visualize how they coordinate. Monitor progress. See what’s happening without micromanaging.
Festivals
See festival progress across everything. Know which phase, which sequence, which task. Monitor execution at whatever level makes sense.
Organizational Workflows
Build systems that automate complex organizational workflows. Not just tasks, but how entire initiatives operate.
Executive Decisions
Make high-level decisions. The abstraction handles team-level complexity. You focus on what matters.
Scale Without Overhead
More agents. More sessions. More campaigns. Same mental overhead. The complexity doesn’t grow linearly with scale.
One person managing what would normally require an entire management layer.
Hundreds of agents. One window. Executive control.
Everyone is focused on model intelligence. AGI. Benchmarks. Who has the best reasoning.
That’s the wrong problem.
Current models can replicate the majority of human tasks. Faster. Often better.
The bottleneck isn’t intelligence. It’s direction.
We build tools and systems problem-by-problem. AI helps, but that approach only scales so far.
There’s no executive layer. No way to direct thousands of capable subsystems toward a unified goal.
Human civilization doesn’t rely on the most intelligent people to do all work. We break work down. Distribute it across the population in manageable chunks. Create governance around it. Direct it toward a vision.
That’s what Obey enables for AI.
The decomposition:
Vision & Strategy
└── Initiatives
└── Projects
└── Goals
└── Subgoals
└── Tasks (smallest reliable chunk)
The abstraction hierarchy:
Obey (Executive layer)
└── Campaigns (Initiatives)
└── Projects (Focused work)
└── Festivals
└── Phases
└── Sequences
└── Tasks
The abstraction goes as deep as needed. Theoretically infinite. Each layer handles its own complexity.
Not smarter AI. Directed AI.
The intelligence exists. Obey makes it useful at scale.
VPs don’t manage 200 people directly. They manage at the right level of abstraction.
Obey applies the same principle to AI agents.
You’re the executive
High-level decisions. Strategic direction. Initiative oversight.
Sessions are your teams
Each session handles its own complexity. Multiple agents working together. You don’t need to track each one.
Campaigns are your divisions
Unified workspaces for related initiatives. Dozens of projects building in parallel with shared context.
Scale without overhead
More agents doesn’t mean more management burden. The abstraction handles the complexity growth.
Right-level thinking
You think about outcomes, not task routing. About initiatives, not individual agent prompts.
Organizational workflows
Build systems that automate how entire initiatives operate. Not just tasks. Entire operational patterns.
One person managing what would normally require an entire management layer.
The complexity exists. It’s just abstracted at the right level so you can actually manage it.
Workspaces for unified initiatives. But you don’t run just one.
Campaigns in parallel
Run 5, 10, or more campaigns simultaneously. Each is an independent initiative with its own context.
Projects per campaign
Each campaign can have dozens of projects building in parallel. Related work that shares context.
Agents per project
One agent per project is often more effective than multiple agents on the same work. But you can configure it either way.
5 campaigns × 5 projects × 1 agent = 25 agents
10 campaigns × 10 projects × 1 agent = 100 agents
10 campaigns × 10 projects × 2 agents = 200 agents
Nothing is limited. Scale as deep as you need.
Multiplicative scale
Small numbers at each level compound into large workforce capacity.
Flexible configuration
More campaigns with fewer agents each. Fewer campaigns with more projects. Whatever matches your work.
Single view
All campaigns. All projects. All agents. One window.
The scale multiplies. The interface stays simple.
Plan every micro decision without actually planning them.
Complex projects have thousands of decisions. Architecture choices. Implementation details. Edge cases. Error handling. Testing strategies.
Planning all of this manually doesn’t scale. You’d spend more time planning than building.
Hierarchical Decomposition
High-level goals decompose into phases. Phases into sequences. Sequences into tasks. Each level handles its own detail.
You define what you want. The system decomposes how to get there.
Quality Gates
Every level has checkpoints. Testing. Review. Validation. Quality is structural, not optional.
You don’t manually verify each step. The structure enforces it.
Execution Tracking
See progress at any level. Know what’s done, what’s in progress, what’s next. All without micromanaging.
Scale Without Complexity
More projects. More parallel work. Same mental overhead. The abstraction handles the growth.
CEO-Level Management
Make high-level decisions. Delegate details to the structure. Focus on what matters.
Consistent Quality
Quality gates run automatically. Nothing ships without passing. No manual checklist required.
Plan at the level that matters. Let abstraction handle the rest.
You’re the CEO of your agents, not their manager.
Your AI organization shouldn’t be tied to one machine. Obey runs where you run.
TUI first
Terminal interface for power users. Keyboard-first. Fast. Where serious work happens.
Mobile follows
iPhone. iPad. Android. Same campaigns, same sessions, same control. Different screen.
Desktop everywhere
macOS. Linux. Windows. The daemon runs on your infrastructure. The client runs anywhere.
Central daemon
Obey daemon runs on your main work machine. The brain of your AI organization.
Remote campaigns
Connect to campaigns running on VPS, other desktops, onsite servers. All appearing local.
Unified view
Doesn’t matter where the compute lives. You see one coherent organization.
Start work on your desktop. Continue on your iPad. Check progress from your phone.
Your AI workforce runs 24⁄7. You manage it from wherever you are.
This R&D created more than architecture. It created a daily development workflow and tool suite that has been used to ship AAA gaming backends, blockchain developer tool suites, trading compliance systems. Niche, deeply technical products that are used daily in production
In some cases, delivered what multiple years and significant capital investment could not, in months while development was done in parallel.
Campaigns and festivals aren’t just concepts. They’re a daily practice. Every project, every feature, every complex system we build is built using these techniques.
The tooling evolved through constant use and actual needs. What works gets refined. What doesn’t gets cut.
Multiple parallel products shipped shipped
Complete developer tool ecosystems, blockchain gaming backends, trading compliance systems with CRM integration. Products that provided real value to companies who had struggled to build these systems for years.
Not demos. Not prototypes. Production systems handling real load, real users, real money.
Compressed timelines
In one case, a system that had over 90M in prior investment was built in 6 months, in another case an app that had 1.5 years of development with nothing useful to show was rebuilt and launched to fully replace a complex manual workflow in 3 months.
The techniques work
Battle-tested daily since March 2025. Refined through real shipping pressure, real engineering workflows, not theoretical research.
Continuous improvement and Daily Feedback
We use our own products on a daily basis, if a subsystem isn’t useful in obey until a later date, we build products we can use or launch based on the subsystem to speed up our feedback loop.
The obey ecosystem improves daily, often with exponential improvements in quality, speed and reduction in token usage for the same result.
Multi-agent chat application. Based on the orchestration system developed for early Obey (guild-core).
Shared context workspace for teams and communities. Built on Obey’s campaign RAG system. Built during Christmas 2025.