AI agent development tutorials: tool calling and reliable workflows

Who is this for, and how should you start?

For developers who can build a basic AI application and want to combine individual tasks into stateful, recoverable workflows.

When should you choose an agent or a fixed workflow?

A fixed workflow follows predefined steps; an agent can propose its next tool call from the task and results. Define boundaries, completion criteria, and permitted tools before deciding whether dynamic selection is needed. A proposed action is not a successful execution: read back actual results.

Implement tool permissions and persistent state

Define parameters, outputs, and permissions for every tool, with authorization validated on the server. Persist states, inputs, and results so work can resume after interruption. Use stable identifiers for writes and external actions. When a result is unknown, read state before retrying to avoid duplicates.

How can a workflow prove reliable operation?

Distinguish recoverable failures from decisions needing human handoff. Bound attempts, duration, and cost. Record necessary task versions, results, timings, and errors without private material or credentials. Verify repeated execution, recovery, and new inputs before preparing operating and maintenance instructions.

Learn in sequence

8 learning units are available. Read, practice, and keep your verification records.

1. Tool calling and permissionsL4 · About 12 min
2. State and step orchestrationL4 · About 15 min
3. Failures, retries, and human handoffL4 · About 18 min
4. Logging and quality evaluationL4 · About 21 min
5. Users and access controlL4 · About 12 min
6. Deployment and configurationL4 · About 15 min
7. Performance, monitoring, and costL4 · About 18 min
8. Updates, operations, and delivery documentationL4 · About 21 min

Test your methods in a project

From one task to a reliable system. Follow the project steps and verify actual deliverables.

Common learning questions

Should failed tool calls always be retried?

Classify parameter, permission, timeout, rate-limit, and parsing errors first. Retry only recoverable failures within limits. Read unknown state first and hand over when necessary.

Are complete MCP and multi-agent courses available?

Current material covers fundamentals of tool calling, permissions, state, and reliable workflows. Dedicated MCP protocol and multi-agent framework courses are not currently provided; use the actual course directory as the reference.