Building Task Agents with Strands SDK
Task agents are the building blocks of any AI system. A well-designed task agent does one thing reliably: classify a ticket, extract entities from a document, generate a SQL query, or call an API.
What Makes a Good Task Agent​
- Single responsibility - One agent, one job
- Tool use - Can invoke external functions (APIs, databases, file systems)
- Structured output - Returns predictable, parseable results
- Error recovery - Handles failures gracefully with retries and fallbacks
- Observability - Logs decisions, tool calls, and outcomes for debugging
Real-World Task Agent Examples​
| Agent | Input | Tools | Output |
|---|---|---|---|
| Ticket Classifier | Support ticket text | None (pure LLM) | Category + priority JSON |
| Document Extractor | PDF content | None | Structured entity JSON |
| API Integrator | User request | REST API calls | API response summary |
| SQL Generator | Natural language query | Database schema lookup | SQL query + results |
| Code Reviewer | Pull request diff | GitHub API | Review comments |
What This Course Covers​
| Module | Topic |
|---|---|
| 1 | Strands SDK fundamentals and agent lifecycle |
| 2 | Defining tools with type-safe schemas |
| 3 | Structured output with Pydantic models |
| 4 | Error handling and self-correction patterns |
| 5 | Testing agents with mock tools |
| 6 | Deploying task agents on Lambda |
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Building Task Agents
Get the complete 6-module course with production task agent implementations, tool definitions, and Lambda deployment patterns.