AdaptIQ — the learning layer for loop engineering

AdaptIQ learns the decisions inside your agent's loop — which action to take next, when to stop, when to escalate — from execution traces, and keeps the resulting policy readable, versioned and reversible.

No gradient updates, no fine-tuning. The policy is a Q-table over (state, action) that you can read, diff and roll back. It is learned offline, with a human validation step, and promoted through paired evaluation.

Built for bounded, repetitive agent workflows — document processing, back-office automation, support flows — where predictability and auditability matter more than raw capability. Not for open-ended coding agents.

Read. Bound. Correct. Read what your agent did against what it was meant to do. Bound how far it can go. Correct its policy in cycles you approve.

Loop engineering is the practice of deliberately designing how an agent iteratively acts, checks and stops (IBM, 2026). Every definition of it so far describes loops designed by hand. AdaptIQ makes the loop learnable — and keeps it legible.

What it does today (v0.12)

• Learns a tabular policy from agent traces. State = current subtask, last action, last outcome, key context. Actions = a discrete menu of strategic actions. Reward = plan adherence + execution success, plus optional external feedback. Offline Q-learning, tabular updates, no model weights touched.
• Reconciles intention with execution. The plan extracted from your prompts is aligned with the actual execution logs, and optional human feedback is folded into the post-run analysis. This is where ground truth — and rewards — come from. A full human validation step, where states and rewards are reviewed and corrected one by one, is a roadmap item, not something the CLI does today.
• Turns the learned policy into better task descriptions for the next run, and applies it at runtime through a decision engine (the setup used in the benchmark).
• Measures every run: tokens, cost, latency. Reports are written locally by default; they are uploaded only if you put an e-mail address in the config.

Supported today: CrewAI as the agent framework (pinned range, see below) · OpenAI gpt-4.1 and gpt-4.1-mini · Python 3.11+ · verified on Windows; Linux and macOS are expected to work and Linux is covered by CI from this release on.

Measured results

Paired benchmark — same agent, same targets, pinned model IDs, with and without AdaptIQ. Full method, data and code: adaptiq-benchmark (DOI 10.5281/zenodo.16876743).

The headline is the first line, not the cost line: the same agent becomes 2.8× more predictable at near-equal quality. The quality delta missed its ≥ 0 target by 0.17 points; it is reported as measured.

Quick start

pip install adaptiq            # or: uv pip install adaptiq

# 1. Scaffold a project (CrewAI is the only supported template today)
#    This creates ./my_project/src/my_project/
adaptiq init --name my_project --template crew-ai --path ./my_project

# 2. Edit the generated config (project name, model, embeddings, framework adapter,
#    and the e-mail address that opts you in to uploaded reports — empty means local only)
#    ./my_project/src/my_project/config/adaptiq_config.yml

# 3. Validate the configuration
adaptiq validate --config_path ./my_project/src/my_project/config/adaptiq_config.yml --template crew-ai

# 4. Run your CrewAI agent as usual — AdaptIQ instruments the run, learns, and writes the reports

Development install:

git clone https://github.com/adaptiq-ai/adaptiq.git && cd adaptiq
python -m venv .venv && source .venv/bin/activate
pip install -e . && pip install pytest
pytest tests

How it works

1. Trace — the agent's execution is logged step by step (thoughts, actions, tool results, outcomes).
2. Reconcile — the intended plan is aligned with the trace; a human validates states, actions and rewards.
3. Learn — a Q-table over (state, action) is updated from the validated trace. Tabular, gradient-free.
4. Apply — the policy shapes the next run: better task descriptions offline, decisions at runtime.
5. Evaluate — paired runs, pinned models, statistical significance, before any policy is promoted.

Details: ARCHITECTURE.md.

Roadmap — loop engineering, learned

One engine, four verbs. Each verb opens only when the previous one has passed its exit benchmark. Full plan with gates and kill criteria: ROADMAP.md.

Principles that will not change: the policy stays a readable table; learning stays offline with a human in the loop; an action is promoted only if its quality delta is ≥ 0 at p < 0.05; the policy is learned over abstract actions, so it survives a change of model or provider.

Limits

• Tabular learning fits bounded, repetitive workflows. It does not fit open-ended agents.
• The state abstraction is hand-designed; generalisation across unrelated tasks is limited.
• Today's integration is CrewAI-only. Framework-agnostic trace ingestion is the first roadmap item.
• Dependencies are pinned to the ranges the code was validated against: crewai < 0.178, because later releases drop an API the CrewAI logger relies on, and langchain < 1.0. Supporting the current lines of both is the first item after the roadmap's audit verb.

Contributing

Issues and pull requests are welcome — bug reports, benchmark reproductions and trace samples from real workflows are the most useful contributions right now. See CONTRIBUTING.md. Updates: @adaptiq_ai.

Citation

First released in July 2025; cite the version you used.

@software{amri_adaptiq_2026,
  author  = {Amri, Wassim},
  title   = {AdaptIQ: a learned control layer for AI agents},
  year    = {2026},
  version = {0.12.9},
  url     = {https://github.com/adaptiq-ai/adaptiq},
  note    = {Benchmark: doi:10.5281/zenodo.16876743}
}

License

Code: Apache 2.0. Policies (Q-tables) learned from your own traces belong to you.