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Claude Code & Custom Agents - From Prompt Engineering to Context Engineering

Speaker: Sal Castoro
Duration: 15 minutes
Time: 6:45 PM - 7:00 PM

Overview​

Sal presented the evolution of agentic coding tools, focusing on Claude Code's approach to context engineering and custom sub-agents. The talk covered how Claude Code addresses LLM limitations through instruction files, custom agents, and parallel execution patterns.

📺 Watch the Talk​

The Evolution: From Prompt to Context Engineering​

The Dark Ages of Prompt Engineering (2022-2023)​

  • Everyone focused on crafting the "perfect prompt"
  • Patterns weren't generalizable across models
  • Inefficient compared to modern tooling
  • Each model had its own quirks and intricacies

The New Era: Context Engineering​

  • MCP (Model Context Protocol) and tooling provide scaffolding around foundational models
  • Claude Code allows model selection (Sonnet, Opus)
  • Focus shifted from prompts to managing context effectively

Solving the Ephemeral Memory Problem​

The Challenge​

  • LLMs are stateless (like REST APIs)
  • Context windows get compacted or cleared
  • Loss of relevant information over time

The Solution: Instruction Files​

  • Claude.md files persist state between requests
  • LLM-friendly format for maintaining context
  • Survives context window compaction/clearing
  • Greater control over context compared to cursor/copilot

Custom Sub-Agents in Claude Code​

Evolution of Agent Capabilities​

  1. Task Command: Early ability to run separate tasks

    • Bash commands
    • Web scraping
    • Role-based sub-agents

  2. Custom Agents Feature (Released 2-3 months ago)

    • Pre-defined sub-agents with own system prompts
    • Don't inherit from Claude.md (isolation)
    • Complete tasks independently
    • Defined using /agents command

Key Features of Custom Agents​

  • Isolated Context Windows: Each agent has its own context
  • Parallel Execution: Run multiple agents simultaneously
  • Tool Selection: Choose specific tools including MCP servers
  • Transportable: Markdown files can move between projects
  • Domain-Specific: Specialized for particular tasks

Best Practices for Custom Agent Development​

General Guidelines​

  1. One Job Per Agent: Keep agents focused
  2. Start Read-Only: Begin with read tools, add editing as needed
  3. Restrict Tools: Minimize context pollution
  4. Clear Descriptions: Help orchestrating agent know when to invoke

Example Agent Types​

  • Code Reviewer: Runs after significant code changes
  • QA Agent: Testing and validation
  • Debugging Agent: Error analysis and fixes
  • Documentation Agent: Generate/update docs

Live Demo: Building an Astro Blog​

Sal demonstrated parallel agent execution for creating a personal site with blog functionality:

  • Multiple agents running simultaneously
  • Each handling specific aspects (components, styling, content)
  • Visual demonstration of parallel processing power

Execution Patterns​

  • Serial Execution: Pass information from agent to agent
  • Parallel Execution: Multiple agents work independently
  • Phase-Based: Organize agents into execution phases

Context Management Considerations​

The Downsides​

  • Token Cost: Each agent consumes context space
  • Context Pollution/Enrichment: Balance between too much and too little
  • Compaction Frequency: More agents = more frequent clearing
  • Quality Impact: Frequent compaction reduces output quality

Optimization Strategies​

  • Keep agent prompts concise
  • Limit number of large sub-agents
  • Monitor context usage with /context command
  • Balance between functionality and token efficiency

Q&A Highlights​

Q: How well do sub-agents work for visual/multi-modal tasks?

  • Mixed results with accessibility testing
  • Can use Playwright MCP for headless Chrome screenshots
  • Image analysis capabilities vary
  • Still a work in progress

Q: How do you determine the atomic unit for an agent?

  • Think like database transactions
  • Group related small tasks together
  • Example: React component creation vs unit testing
  • Keep related functionality cohesive

Key Takeaways​

  1. Context engineering > Prompt engineering for modern LLM applications
  2. Custom agents provide isolation and prevent context pollution
  3. Parallel execution dramatically speeds up complex tasks
  4. Balance is key: Too many agents cause token overhead
  5. Claude Code gives fine-grained control over context and execution

Resources​

About the Speaker​

Sal Castoro is an AI engineer specializing in agentic terminal-based tools and Claude Code implementations. He actively explores the boundaries of context engineering and multi-agent orchestration patterns.