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Building-AI-Agents-From-Zero-To-Production / pt-PT

AGENTS.md194 starsChanged 4 months agoMITView on GitHub

Orientações para agentes de codificação de IA (e colaboradores humanos) que trabalham neste repositório. Se és um agente automatizado a fazer alterações aqui, lê este ficheiro primeiro e segue-o. Building AI Agents from Zero to Production é um curso de aprendizagem da Microsoft. Ensina os programadores a conceber, desenvolver, avaliar, implantar e operar agentes de IA na Microsoft Foundry usando o Microsoft Agent Framework (MAF). O conteúdo está organizado como uma sequência de lições, cada uma com um README.md e…

  • Reads credentials
  • Installs packages

We split this AGENTS.md into the rules it gives an agent

5 rules. About 0 are common, 1 is distinctive, and 4 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈1

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 4

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  • could be automatic: a setting could enforce it
  1. Very specific:

    Never commit secrets: Only *.env.example files are tracked.

    line 27
  2. Very specific:

    No deprecated models: Use gpt-5.1 for chat/eval and gpt-5-codex for coding.

    line 32
  3. Very specific:

    Do not introduce gpt-4o, gpt-4.1, or any retired model, and do not use *GitHub Models* (retiring July 30, 2026) — all models are served …

    line 33
  4. Distinctive:

    Write focused commits with clear, imperative messages.

    line 86
  5. Very specific:

    Do not commit generated caches, virtual environments, or .env files (all git-ignored).

    line 91

≈ “Common” is an estimate: at least 1 in 5 of the 36,368 AGENTS.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
AGENTS.md

AGENTS.md

Orientações para agentes de codificação de IA (e colaboradores humanos) que trabalham neste repositório. Se és um agente automatizado a fazer alterações aqui, lê este ficheiro primeiro e segue-o.

What this repository is

Building AI Agents from Zero to Production é um curso de aprendizagem da Microsoft. Ensina os programadores a conceber, desenvolver, avaliar, implantar e operar agentes de IA na Microsoft Foundry usando o Microsoft Agent Framework (MAF). O conteúdo está organizado como uma sequência de lições, cada uma com um README.md e exemplos Python executáveis.

lesson-1-agent-design/            Use case + how to design effective agents
lesson-2-agent-development/       Build specialised agents with MAF (multiple runnable samples)
lesson-3-agent-evals/             Evaluations and observability
lesson-4-agentdeployment/         Deploy a hosted agent + OpenAI ChatKit front end
lesson-5-hosted-agents-production/ Hosted Agents vs Capability Hosts, BYO storage, governance
lesson-6-toolbox/                 Microsoft Toolbox: define + govern tools centrally
lesson-7-multi-agent-a2a/         Multi-agent orchestration over the A2A protocol

Root docs: README.md (start here), MIGRATION-GUIDE.md (SDK migration detail), CHANGELOG.md.

Golden rules

  1. Never commit secrets. Only *.env.example files are tracked; real .env files are git-ignored. Do not hardcode endpoints, keys, tokens, or connection strings in samples or docs.
  2. Do not touch translations/ or translated_images/. These are generated automatically by a translation GitHub Action. Never hand-edit them; make source changes in the top-level lesson files only.
  3. No deprecated models. Use gpt-5.1 for chat/eval and gpt-5-codex for coding. Do not introduce gpt-4o, gpt-4.1, or any retired model, and do not use GitHub Models (retiring July 30, 2026) — all models are served through Microsoft Foundry.
  4. Use the current SDK surface. Samples target agent-framework (pinned in requirements.txt) with FoundryChatClient and the Responses API. Do not reintroduce the older AzureAIClient / AzureAIAgentClient / AzureOpenAIChatClient patterns.
  5. Keep terminology current: Microsoft Foundry (not "Azure AI Foundry"), Microsoft Agent Framework, Hosted Agents, Capability Hosts, Microsoft Toolbox, MCP / Hosted MCP, A2A.

Setup

python -m venv .venv
# Windows:  .venv\Scripts\Activate.ps1
# macOS/Linux:  source .venv/bin/activate
pip install -r requirements.txt

az login                     # os exemplos autenticam-se com a sua identidade de desenvolvedor
cp .env.example .env         # depois preencha o endpoint e o modelo do seu projeto Foundry

Requirements: Python 3.12+, the Azure CLI, and access to a Microsoft Foundry project with a deployed GPT-5-series model. Each lesson README lists its own prerequisites and the env vars it needs (see the lesson-level .env.example where present).

Running samples

Most lesson-2 samples launch a local DevUI on a dedicated port (for example 8090–8096); the A2A server in lesson 7 listens on port 9000. Check each sample's docstring/README for the exact command and port. Because samples call live Foundry endpoints, they need a valid .env and az login.

Validating changes

There is no unit-test suite; validation is static + live:

  • Static gate (must pass before commit): byte-compile every sample.
    python -m py_compile $(git ls-files '*.py')
    No Windows PowerShell:
    git ls-files '*.py' | ForEach-Object { python -m py_compile $_ }
  • Links Markdown: the CI static job runs markdown-link-check (config: .github/workflows/markdown-link-check-config.json). Verifica que quaisquer novos links externos resolvem (HTTP 200).
  • Smoke test: .github/workflows/smoke-test-hosted-agent.yml runs the AI Smoke Test action against a deployed hosted agent (workflow_dispatch, OIDC). Live agent runs require Azure access.

CI (static job) auto-discovers .py files, so new samples are covered without editing the workflow. Do not commit code that fails py_compile.

Commit conventions

  • Write focused commits with clear, imperative messages.
  • Include the co-author trailer on agent-assisted commits:
    Co-authored-by: Copilot App <223556219+Copilot@users.noreply.github.com>
  • Do not commit generated caches, virtual environments, or .env files (all git-ignored).

Where to make specific changes

AlteraçãoLocalização
Narrativa do curso / texto da liçãolesson-*/README.md (source only — never translations/)
Código executávellesson-*/**.py, setup_vector_store.py
Dependênciasrequirements.txt (mantém as versões fixas)
Documentação das variáveis de ambiente.env.example, ao nível da lição .env.example
CI / gate estático.github/workflows/
Competências do curso para assistentes de IA.github/skills/

<!-- CO-OP TRANSLATOR DISCLAIMER START -->

Aviso Legal: Este documento foi traduzido utilizando o serviço de tradução automática Co-op Translator. Embora nos esforcemos pela precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte autorizada. Para informações críticas, recomenda-se tradução profissional humana. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes da utilização desta tradução.

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