Dashboard / Planner🟡 Engine Offline

Goal decomposition in the planning engine

The planner transforms objectives into staged execution paths with advanced scoring, resource budgets, and recovery strategies.

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Total Inferences

Reasoning cycles completed

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Active Experts

Mixture-of-experts modules

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Memory Entries

Short + Long term

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Permanent Memory

Frozen skill memories

Submit a Goal

Advanced Planning

Send an objective to the planning engine. It will decompose the goal into steps using advanced scoring and return an execution plan.

Dependency Graph

Structure

Plans are decomposed into prerequisite trees. The engine identifies parallelizable branches and serial bottlenecks.

Prerequisites rendered as living graphs
Parallel vs serial task identification
Fragile dependency detection before execution

Plan Scoring

Algorithm

Each candidate plan is scored across utility, cost, and feasibility dimensions. The top-K beams are retained and refined.

Multi-objective scoring function
Top-K beam retention with pruning
Iterative refinement until convergence

Resource Budget

Budgets

Plans are evaluated against latency, compute, and safety budgets before execution. Over-budget steps trigger rerouting or human escalation.

Token, tool, and review cost estimation
Per-tenant policy budget profiles
Over-budget detection and escalation

Recovery Strategies

Fallback

Fallback paths let the system recover when tools fail or conditions change mid-run. Recovery outcomes improve future planning heuristics.

Backup plans for common failure modes
Retry / reroute / escalate decision logic
Recovery outcomes feed back into heuristics