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
- Very specific: line 27
Never commit secrets: Only *.env.example files are tracked.
- Very specific: line 32
No deprecated models: Use gpt-5.1 for chat/eval and gpt-5-codex for coding.
- Very specific: line 33
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 …
- Distinctive: line 86
Write focused commits with clear, imperative messages.
- Very specific: line 91
Do not commit generated caches, virtual environments, or .env files (all git-ignored).
≈ “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.
Files
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 protocolRoot docs: README.md (start here), MIGRATION-GUIDE.md (SDK migration detail), CHANGELOG.md.
Golden rules
- Never commit secrets. Only
*.env.examplefiles are tracked; real.envfiles are git-ignored. Do not hardcode endpoints, keys, tokens, or connection strings in samples or docs. - Do not touch
translations/ortranslated_images/. These are generated automatically by a translation GitHub Action. Never hand-edit them; make source changes in the top-level lesson files only. - No deprecated models. Use
gpt-5.1for chat/eval andgpt-5-codexfor coding. Do not introducegpt-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. - Use the current SDK surface. Samples target
agent-framework(pinned inrequirements.txt) withFoundryChatClientand the Responses API. Do not reintroduce the olderAzureAIClient/AzureAIAgentClient/AzureOpenAIChatClientpatterns. - 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 FoundryRequirements: 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.
No Windows PowerShell:python -m py_compile $(git ls-files '*.py')git ls-files '*.py' | ForEach-Object { python -m py_compile $_ } - Links Markdown: the CI
staticjob runsmarkdown-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.ymlruns 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
.envfiles (all git-ignored).
Where to make specific changes
| Alteração | Localização |
| Narrativa do curso / texto da lição | lesson-*/README.md (source only — never translations/) |
| Código executável | lesson-*/**.py, setup_vector_store.py |
| Dependências | requirements.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.
<!-- CO-OP TRANSLATOR DISCLAIMER END -->Where files are headingNew AGENTS.md files have more rules about keeping secrets out of the code, and fewer about code style.
Of the AGENTS.md files we read, the 28,693 first seen in the last six months, against the older ones: the kind of rule that gained most share, and the kind that lost most. About files like this in general, not this one, and an estimate, since a kind is a pattern in the words.
In this repository24 other files it gives its agents.
- AGENTS.mdAGENTS.md
- translations/bn/AGENTS.mdAGENTS.md
- translations/cs/AGENTS.mdAGENTS.md
- translations/da/AGENTS.mdAGENTS.md
- translations/en/AGENTS.mdAGENTS.md
- translations/es/AGENTS.mdAGENTS.md
- translations/hu/AGENTS.mdAGENTS.md
- translations/id/AGENTS.mdAGENTS.md
- translations/ko/AGENTS.mdAGENTS.md
- translations/lt/AGENTS.mdAGENTS.md
- translations/mr/AGENTS.mdAGENTS.md
- translations/ms/AGENTS.mdAGENTS.md
- translations/nl/AGENTS.mdAGENTS.md
- translations/pa/AGENTS.mdAGENTS.md
- translations/pt-BR/AGENTS.mdAGENTS.md
- translations/th/AGENTS.mdAGENTS.md
- translations/tl/AGENTS.mdAGENTS.md
- translations/vi/AGENTS.mdAGENTS.md
- agent-evaluationSkill
- foundry-agent-basicsSkill
- hosted-agent-deploymentSkill
- mcp-integrationSkill
- microsoft-toolboxSkill
- multi-agent-a2aSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

