Ralv.ai — Starcraft for AI Agents

ralv.ai is a 3D spatial interface for managing AI agent swarms. the pitch: terminals break down at 20+ agents. gaming UX solved the “control many units” problem decades ago. why not use it for AI orchestration?

created by dominik scholz (@dom_scholz ).

first spotted in signals — 2026-02-10 .

→ ralv.ai

the problem

you have 20 AI agents running. different tasks. different repos. different states.

how do you manage them?

tabs? you run out of screen real estate.
split terminals? good luck past 6.
tmux? congratulations, you’re a sysadmin again.

terminals were designed for one process. maybe two with multiplexing. they’re fundamentally serial interfaces trying to handle parallel workloads.

at 50+ agents, terminals become the bottleneck.

the insight

the gaming industry spent decades and billions of dollars solving this exact problem: how does one human intuitively control hundreds of units?

RTS games (Starcraft, Age of Empires, Command & Conquer) nailed it: → spatial canvas (you see all units at once)
→ drag-select (group selection without typing)
→ zoom levels (strategic view ↔ tactical execution)
→ mini-map (spatial awareness of the whole battlefield)
→ unit states visible at a glance (health, activity, assignments)

why are we managing AI agents with text streams when gaming already solved the UX?

how it works

ralv.ai applies RTS interface patterns to agent orchestration:

spatial canvas: agents appear as units on a 3D map. you can see the swarm.

drag-select: click and drag to select multiple agents. assign them to tasks as a group.

zoom controls: zoom out for strategic overview. zoom in to watch individual agent execution.

task deployment: drag agents to objectives. assign roles. monitor progress.

real-time execution view: see what each agent is doing. which are idle. which are stuck. which completed tasks.

the metaphor is RTS commander, not sysadmin.

the crypto angle

ralv has a token (STARCRAFT on CryptoRank). the economics layer for agent marketplaces.

the thesis: if agents are autonomous economic actors, they need a marketplace. tokens enable agent-to-agent transactions, skill licensing, compute trading.

not the main value prop. but worth noting. the intersection of agent swarms and token economics is coming.

why this matters now

stripe ships 1000+ agent-written PRs per week (signals — signal 5 ).

skills catalogs are exploding (signals — signal 3 ).

we’re entering the “how do you manage this many agents?” phase.

the orchestration UX problem is wide open. terminals work for 1-3 agents. at 50+, you need spatial awareness.

ralv is the first serious proposal for agent swarm interfaces.

the open questions

will developers accept spatial interfaces?
terminal users are famously resistant to GUIs. “I can do it faster with grep and tmux.” maybe. but can you do it faster with 50 agents?

does the RTS metaphor hold at scale?
Starcraft handles ~200 units. what about 1000 agents? 10,000? does the interface collapse or evolve?

who builds the ecosystem?
ralv is early. the real question: who builds the connectors, plugins, and integrations? can you drop this into an existing agent setup or does it require greenfield?

self.md take

the agent orchestration UX problem is real.

right now we manage agents like servers: ssh into them, check logs, restart when something breaks. but agents aren’t servers. they’re swarms.

and swarms need spatial interfaces, not text streams.

gaming figured this out 30 years ago. RTS games gave humans intuitive control over hundreds of units. someone finally asked “what if we just used game UI for agents?”

the answer: it works.

the question is whether developers will accept it — or keep pretending terminals scale to 50+ agents.

my guess: the first time you’re managing 30 agents and one goes rogue, you’ll reach for a spatial interface. because grep and tmux won’t save you.

ralv might not win. but the category — “how humans manage agent swarms” — is about to explode.

related

→ AI agent skills catalogs — the skills layer for agent customization
→ AI coding assistants — the foundation layer (Cursor, Claude Code, Copilot)
→ signals — programming languages for agents — where ralv.ai was first spotted