AI agents that build institutional memory.
You describe the problem. WinDAGs builds the team.
Watch a real workflow decompose into specialist agents, execute in parallel waves, and recover from failure — all in real time.
You describe the problem. WinDAGs builds the team.
Specialist agents execute in parallel waves and recover from failure — all in real time.
THE PROBLEM WITH AI AGENTS
Today's agent frameworks give you parallelism without intelligence. Speed without safety. Here's what's missing.
NO LEARNING
Agents remember conversations but never learn which approaches actually work. No quality tracking. No skill ranking. Same strategy every time.
NO WARNING
Agents charge ahead without checking their work. No quality gates. No cost estimates. No approval step.
NO ADAPTATION
When something fails, the retry is identical. No strategy change. No fallback. Just the same mistake twice.
UNDER THE HOOD
Two views of the same intelligence. Pipeline shows the learning loop. Execution shows the live DAG.
INTELLIGENCE, NOT JUST SPEED
Other frameworks give you parallelism. WinDAGs gives you a system that learns, warns, adapts, and stays within budget.
LEARNING LOOP
Every run teaches the system what works for YOUR codebase. A 5-step cascade matches skills by signature, context, and domain — then Thompson sampling promotes what actually performs.
RISK ANALYSIS
Every node gets a risk score before execution. Contradiction detection catches conflicting agent outputs before they merge.
RESILIENCE
Exponential backoff, model fallbacks, and automatic retries. When node 7 fails, node 8 doesn't. The DAG adapts.
COST CONTROL
See estimated cost BEFORE execution. Set budgets per node. Model tier selection minimizes spend without sacrificing quality.
HONEST COMPARISON
We respect these frameworks. Here's where WinDAGs differs.
| Feature | WinDAGs | LangGraph | CrewAI | AutoGen |
|---|---|---|---|---|
| DAG-based task orchestration | ||||
| Cross-session memory | ||||
| Statistical skill quality learning | ||||
| 5-step skill selection cascade | ||||
| Human-in-the-loop gates | ||||
| Cost estimation before execution | ||||
| Contradiction detection | ||||
| Wave-based parallelism | ||||
| 200+ curated skill library | ||||
| Zero token overhead (CLI agents) | ||||
| Risk analysis per node | ||||
| Open source (BSL 1.1) |
Comparison based on public documentation as of Mar 2026. We may be wrong — PRs welcome.
HOW IT WORKS
From natural language to parallel execution in four steps.
DESCRIBE
Tell WinDAGs what you want in plain English. No jargon required.
DECOMPOSE
Your request gets broken into subtasks automatically. DAGs form.
MATCH
A 5-step cascade selects the best skill: signature compatibility, context conditions, domain relevance, pattern recognition, then statistical ranking.
EXECUTE
Agents run in parallel waves. Outputs flow forward. You stay in control.
200+ SKILLS
ONE TEAM
Skills are expertise packages. They get injected into agents at runtime, turning generic Claude into a specialist. You don't pick skills—WinDAGs matches them automatically.
Browse All SkillsBETA TESTING SPRING 2026
Be among the first to orchestrate AI agent teams with WinDAGs.
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WHY WINDOWS 3.11?
We didn't pick Windows 3.1 randomly. Windows for Workgroups was Microsoft's first attempt at cooperative multitasking and network-aware computing.
WinDAGs is the same idea, 30 years later, for AI agents.
It's not a joke. It's a statement.
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