v0.9.0

0.9.0 - 2026-10-18

Delivers roadmap milestone v0.7 (ModelFit).

Added — model recommendations (agenticlens.modelfit)

• Experiment evidence: load_experiment_evidence reads an experiment report
  (agenticlens experiment run --save) and records its file name and
  SHA-256; collect_candidates turns every variant into a candidate with one
  attempt per case per trial. Failed trials count as failed attempts with no
  cost. Model names come from the model factor (model_factor); provider,
  deployment, and capabilities from the config's candidates catalog, the
  manifest's variant metadata, or provider/deployment factors.
• Task taxonomy: TaskCategory selects cases by tag, metadata.domain, or
  case ID; derive_categories creates one category per tag when none are
  configured; the all category covers every case; resolve_taxonomy
  reports uncategorized cases and categories that match nothing.
• Hard constraints: ModelConstraints (min_pass_rate,
  min_pass_rate_lower_bound, max_cost_per_case_usd,
  max_cost_per_success_usd, max_mean_latency_ms, max_p95_latency_ms,
  min_consistency, min_safety_pass_rate, allowed_providers,
  blocked_providers, allowed_deployments, required_capabilities) with
  config defaults and per-category overrides (null removes a default,
  including for list constraints). check_constraints fails closed: a limit
  the evidence cannot verify is a violation, including a block-list against
  an unknown provider and a latency limit over partial latency evidence.
• Scoring: ScoringWeights (quality, cost, latency, reliability; per-category
  weights allowed) over feasible candidates, normalised to the cheapest and
  fastest; per-category pareto_frontier (pass rate, cost per success,
  latency) and cost_per_success_ranking.
• Recommendations: recommend_models returns a ModelFitReport with one
  CategoryResult per category. Each Recommendation has a status
  (recommended, no_feasible_model, no_evidence), a confidence level
  with reasons, an explanation citing measured values and every rejection,
  ranked alternatives, a fallback on a different provider when one is
  feasible, a SavingsEstimate against the reference variant (per case, per
  success, and per month from monthly_volume, with attempts, case IDs, and
  source indexes; unavailable when cost is incomplete or the two candidates
  were measured on different cases), and QualityRisks
  (quality regression with significance, regressed, flaky, and safety-failed
  cases, low evidence, unavailable cost, failed trials, no fallback).
• Task-level model matrix: ModelFitReport.matrix (one row per category and
  candidate, with its role), render_matrix_csv, and render_markdown.
• Config and report I/O: load_modelfit_config (YAML or JSON),
  load_modelfit_report, save_modelfit_report.

Added — CLI

• agenticlens modelfit recommend REPORT... [--config] [--manifest] [--save] [--markdown] [--csv] [--json] [--fail-on-unfit] [--fail-on-low-confidence].
  Exit 8 when a category has no feasible model (--fail-on-unfit) or a
  recommendation is low confidence (--fail-on-low-confidence).
• agenticlens modelfit matrix MODELFIT_REPORT [--csv] [--markdown] and
  agenticlens modelfit taxonomy REPORT... [--config] [--json].

Added — schemas, example, docs, CI

• schemas/modelfit/modelfit-config.schema.json and
  schemas/modelfit/modelfit-report.schema.json (schema_version: "1.0").
• examples/modelfit_demo.py: a recorded four-model experiment (two
  providers, cloud and on-prem) on a ten-case support suite, with committed
  artifacts reproduced by tests/test_examples_modelfit.py.
• ModelFit guide; README, roadmap, audit, and AGENTS
  updates.
• CI modelfit job: replays the experiment and runs
  modelfit recommend --fail-on-unfit.

Ecosystem

• No AIOS object or existing Lens artifact changes. Agentic Sidecar 0.8.4
  widens its optional agenticlens extra from <0.9 to <1; MCP 0.3.11
  pins hosted images to 0.9.0 and adds a read-only lens.modelfit_recommend
  tool; Control Tower, Chaos, and Evals already admit 0.9.0.