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Anthropic Claude Agent SDK: Build Autonomous AI Agents
ஆந்த்ரோபிக் கிளாட் ஏஜென்ட் SDK: தன்னாட்சி AI ஏஜென்ட்களை உருவாக்குங்கள்
Explain the step-by-step lifecycle of an agent session, from prompts to final results.
- Break down the core concepts for someone new to building agents with the SDK.
- Describe how developers should handle different message types for building user interfaces.
- Explain how enabling partial message streaming changes the visibility of the agent loop.
- Provide examples of using hooks to intercept and control agent behavior at runtime.
- Summarize how prompt caching helps reduce total costs during long agent sessions.
- Compare synchronous and asynchronous approaches for handling agent tool execution turns.
- Describe how to implement real-time streaming to display text and tool calls.
- Explain how to configure permissions and settings sources to isolate agent environments.
- Detail safety patterns for production deployments including permission isolation and tool approval flows.
- Describe efficient workflows for testing and deploying agents in isolated environments.
- Offer best practices for developers building production agents with custom tools and MCP.
- Explain how permission modes like acceptEdits ensure safety when agents interact with files.
- Describe how to implement tool approval callbacks to verify actions before they run.
- Explain how to use the PreCompact hook to archive history before context summarization.
- List common diagnostic steps when tool calls fail or produce unexpected output.
- Outline methods for monitoring system health and usage metrics in multi-tenant production systems.
- Highlight how to use OpenTelemetry to improve visibility into complex agent sessions.
- Describe how to use subagents for handling complex tasks while keeping context lean.
- Detail strategies for managing long sessions to keep context efficient and costs low.
- Summarize how automatic context compaction prevents hitting window limits during long runs.
- Discuss how token usage and costs are tracked throughout the loop.
- Detail how to monitor cumulative session costs and analyze token usage metrics.
- Detail how to interpret total cost and token usage data from ResultMessage fields.
- Explain how to select effort levels to optimize token costs for different tasks.
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By Sivakumar Viyalanஆந்த்ரோபிக் கிளாட் ஏஜென்ட் SDK: தன்னாட்சி AI ஏஜென்ட்களை உருவாக்குங்கள்
Explain the step-by-step lifecycle of an agent session, from prompts to final results.
- Break down the core concepts for someone new to building agents with the SDK.
- Describe how developers should handle different message types for building user interfaces.
- Explain how enabling partial message streaming changes the visibility of the agent loop.
- Provide examples of using hooks to intercept and control agent behavior at runtime.
- Summarize how prompt caching helps reduce total costs during long agent sessions.
- Compare synchronous and asynchronous approaches for handling agent tool execution turns.
- Describe how to implement real-time streaming to display text and tool calls.
- Explain how to configure permissions and settings sources to isolate agent environments.
- Detail safety patterns for production deployments including permission isolation and tool approval flows.
- Describe efficient workflows for testing and deploying agents in isolated environments.
- Offer best practices for developers building production agents with custom tools and MCP.
- Explain how permission modes like acceptEdits ensure safety when agents interact with files.
- Describe how to implement tool approval callbacks to verify actions before they run.
- Explain how to use the PreCompact hook to archive history before context summarization.
- List common diagnostic steps when tool calls fail or produce unexpected output.
- Outline methods for monitoring system health and usage metrics in multi-tenant production systems.
- Highlight how to use OpenTelemetry to improve visibility into complex agent sessions.
- Describe how to use subagents for handling complex tasks while keeping context lean.
- Detail strategies for managing long sessions to keep context efficient and costs low.
- Summarize how automatic context compaction prevents hitting window limits during long runs.
- Discuss how token usage and costs are tracked throughout the loop.
- Detail how to monitor cumulative session costs and analyze token usage metrics.
- Detail how to interpret total cost and token usage data from ResultMessage fields.
- Explain how to select effort levels to optimize token costs for different tasks.