Grok 4.5 powered AHE for wizard.
Runs wizard on a Docker task set, reads the transcripts, and has Grok 4.5 rewrite the harness bundle (system prompt, tool descriptions, skills, subagents). Measure again. Keep the wins, roll back the rest. Human review, then bake the bundle back into wizard as the new baseline.
Grok 4.5 is the default brain for both wizard and the evolve agent. Any OpenAI-compatible endpoint still works.
Fork of Agentic Harness Engineering by Curry09 (Jiahang Lin) and contributors. Methodology, evolve/debugger agents, and framework are theirs.
- Upstream: https://github.com/china-qijizhifeng/agentic-harness-engineering
- Paper: arXiv:2604.25850
- MIT (see LICENSE)
agents/wizard_harness/is the evolve target (wizard harness export).evolve.pycopies it into a git-tracked workspace; each edit is a commit.- The harbor adapter drops the wizard binary + candidate bundle into each task container and runs
wizard -p. - Verifiers score rollouts. Bad edits roll back.
- Task images come from the Nix
wizard-ahe/task-basebuild.
bash <(curl -fsSL https://raw.githubusercontent.com/teddytennant/wizard-ahe/main/install.sh)Or ./install.sh from a checkout. Builds the ahe CLI, runs ahe setup (pick API, ChatGPT OAuth, or xAI/Grok 4.5), installs wizard, builds the task image.
ahe setup # idempotent
ahe run --config wizard-smoke # wiring gate
ahe run # 10 tasks x 5 iterations
ahe run -i 2 --tasks even-sum,csv-report -k 2
ahe run --no-progress --json
ahe run --dry-run
ahe statusFull runbook and backends: docs/WIZARD-AHE.md.
agents/
wizard_harness/ evolve target (exported harness bundle)
wizard_agent/ harbor adapter
evolve_agent/ AHE meta-agent
configs/ base.yaml + exp-wizard*.yaml
dataset/wizard/ 10 agentic coding tasks
dataset/local-sample/ hello-file smoke task
cli/ ahe (Rust)
docs/WIZARD-AHE.md runbook
scripts/ helpers, OAuth proxies, image build
install.sh one-liner
evolve.py evaluate -> analyze -> improve
flake.nix task-base image + dev shell