What

Hello, Agentic world!

This is a bounded system, an agent whose actions, evidence, permissions, and failures can be inspected and evaluated.

receive a goal → choose an action → use a tool → observe the result → update its state → stop with evidence

How

This repository builds that system in five stages. Each stage extends the same agent loop and adds one capability, one failure mode, and one evaluation layer.

One agent, five stages

Stage	Git branch	Capability added	Failure addressed
1	stage/1-hello-agent	Bounded tool-use loop	Invented actions and results
2	stage/2-file-detective	Investigation and evidence	Unsupported conclusions
3	stage/3-self-correcting	Feedback-driven repair	Repeating a failed approach
4	stage/4-persistent	Checkpoints and retrieval	Lost context after interruption
5	stage/5-governed	Policy, approval, verification	Unsafe or dishonest autonomy

Every stage is cumulative. main contains the completed Stage 5 implementation.

Learn by doing

Start Stage 1 from the empty foundation:

git switch --detach v0
git switch -c work/1-hello-agent

Read STAGE.md, implement the requirements, and run the tests and evaluations. Compare your result only after finishing:

git diff v1 -- src tests evals

For the next stage, start from the previous completed checkpoint:

git switch -c work/2-file-detective v1

Branches are browsable solutions. Tags v0 through v5 are immutable learning checkpoints.

Run locally

Requirements:

• Python 3.12+
• uv
• Ollama
• qwen3:8b as the default controller model

uv sync
ollama pull qwen3:8b
uv run pytest
uv run python -m hello_agentic_world "your task"
uv run python -m evals.run --stage 1

Non-negotiable boundary

The model may propose actions. It may not execute them directly.

The host program must:

• validate tool names and arguments;
• enforce workspace and authorization boundaries;
• execute tools and record observations;
• enforce action, time, and retry budgets;
• reject completion claims not supported by evidence.

The evaluator is independent from the agent and computes ground truth directly.

Repository map

src/          agent implementation
tests/        deterministic unit and integration tests
evals/        scenario fixtures, scorers, and repeated-run evaluation
docs/         curriculum, contracts, architecture, and workflow
workspace/    the only filesystem area available to the agent
runs/         generated traces and reports; not committed

See:

• docs/CURRICULUM.md
• docs/REPOSITORY_STRUCTURE.md
• docs/BRANCH_WORKFLOW.md
• docs/EVALUATION.md

What this work is not

No agent framework, multi-agent role play, open shell, browser control, production credentials, or autonomous network access. Those increase surface area before the core loop is understood.

The outcome is not “an LLM that calls tools.” It is an agent whose actions, evidence, permissions, and failure modes can be inspected and evaluated.