WHAT WINDAGS DOES

Six capabilities that separate WinDAGs from every other multi-agent framework. Each one builds on the others.

THE LEARNING LOOP

Most orchestration frameworks treat every run as a blank slate. WinDAGs remembers. Every execution feeds back into the system — which skills worked, which failed, what combinations produced the best results foryour codebase, your domain, your patterns.

How It Learns
1.Observe — Every skill execution records success rate, latency, cost, and output quality.
2.Rank — Skills that perform well get promoted. Underperformers get flagged. Bayesian inference solves the classic multi-armed bandit problem: explore new approaches or exploit what already works?
3.Adapt — Winning patterns inform future decisions. The system gets better at matching the right skill to the right problem over time.

The more you use WinDAGs, the faster and cheaper it gets. That's the learning loop.

ADAPTIVE PLANNING

WinDAGs doesn't build the entire DAG up front and pray. It uses progressive revelation: start with what you know, leave placeholder steps for later, and refine the plan wave-by-wave as information arrives.

Progressive Decomposition
Pass 1Structure — Break the task into logical subtasks. Identify dependencies.
Pass 2Capability — Match each subtask to skills. Flag gaps.
Pass 3Topology — Arrange into parallel waves. Maximize concurrency.

Result: 8x reduction in context-switching overhead compared to sequential agent chains.

FOUR-LAYER QUALITY

Every piece of agent output passes through four quality layers before it gets merged into your project.

Floor

Minimum bar. Does the output parse? Does it compile? Does it match the expected format? Rejects gibberish before it wastes downstream time.

Wall

Correctness check. Does the output actually solve the subtask? Tests run, assertions checked, requirements matched.

Ceiling

Quality standard. Is it well-written? Does it follow project conventions? Could a human approve it without changes?

Envelope

Cross-node coherence. Does this output conflict with what other agents produced? Contradiction detection catches integration bugs early.

INTELLIGENT FAILURE HANDLING

Agents fail. Models hallucinate. APIs time out. WinDAGs classifies every failure across four dimensions and routes it to the right recovery strategy.

4D Failure Classification
DimensionOptions
SeverityWarning, Error, Fatal
ScopeNode, Wave, DAG
RecoverabilityRetry, Reroute, Escalate
CauseModel, Network, Logic, Human

Escalation ladder: retry with backoff, fall back to a cheaper model, reroute to a different skill, or pause and ask the human. The DAG never silently fails.

COST TRANSPARENCY

Every node in the DAG has a cost estimate before execution starts. Set budgets per node, per wave, or per DAG. Get drift alerts when spending trends upward.

Cost Dashboard
►Per-node tracking — See exactly how much each agent costs. Opus for hard tasks, Haiku for simple ones.
►Projection engine — Before you hit “run,” see the estimated total. Adjust the model mix to fit your budget.
►Drift alerts — If a recurring workflow starts costing more, you'll know immediately.

See the receipt. Every token, every model, every retry.

200+ CURATED SKILLS

Skills are expertise packages: prompts, tool configurations, and domain knowledge bundled into reusable units. WinDAGs ships with 200+ of them and the library keeps growing.

Skill Lifecycle
1.Authored — Written by humans with domain expertise. Reviewed for accuracy and safety.
2.Matched — A 5-step selection cascade automatically picks the right skill for each subtask. No manual configuration.
3.Improved — The learning loop tracks which skills perform best. Underperformers get flagged for revision.

Browse All Skills

WANT TO TRY THIS?

WinDAGs is in beta testing Spring 2026. Request early access and be among the first to use it.

Request Early Access