Check for updates

The interface is a thin front door: you type intent; the agents quietly parse it, plan a minimal sequence, call deterministic numerical solvers, check the numbers, and reply—no ad-hoc scripts. The agent handles intent and entity extraction (e.g., case id, buses, MW changes, outage scope) and sketches a compact plan. It then orchestrates typed tool calls for ACOPF, topology or load edits, and contingency sweeps, then layers validation: convergence flags, power balance tolerance, operating limits, and sanity checks on modified elements. A structured context keeps the latest solved state, applied diffs, and cached contingency fragments so only affected layers are recomputed. Every reported number is pulled from stored structured results, making the reply auditable and reproducible with timestamps and call metadata. New analytical tools can be registered with a schema; the planner notices capabilities without refactoring core logic. The result is a deterministic loop—parse, plan, invoke, validate, narrate, persist—that preserves engineering rigor while trimming the friction of multi-step exploratory studies.

Agents collaborate through a single structured, versioned session state capturing: (a) the active network plus incremental diffs (load shifts, outages, parameter edits), (b) validated numerical artifacts (latest feasible ACOPF solution, cached power-flow/per-contingency snapshots), and (c) provenance (solver options, timestamps, tool versions, validation flags). After a solve, the ACOPF agent deposits a typed ACOPFSolution (dispatch, objective cost, losses, voltage extrema, branch loadings, constraint margins) instead of only prose. The CA agent inspects freshness against the diff log to decide whether it can reuse that base point or must trigger a selective re-solve. Cross-agent transfer is strictly schema-bound (ACOPFSolution, ContingencyResultSet) so planning references concrete fields (e.g., base_objective_cost, min_voltage_pu, max_thermal_loading) and avoids hallucination. Each outage evaluation is cached under a composite key (case + outage + diff hash); criticality ranking streams those cached records to detect recurring overload corridors or voltage depressions and writes back ranked justifications as structured data plus a derived human summary. A normalized change log appends every modification; agents replay only relevant diffs to reconstruct required state. Session persistence serializes baseline, diffs, artifacts, contingency cache, and rankings for seamless resumption. This disciplined produce-validate-consume loop lets compound requests (“solve, assess T-1 risk, rank reinforcements”) execute efficiently, coherently, and reproducibly.