Engineering

Advanced Prompt Engineering for Claude in Agentic Coding Workflows

Allan Porras

Allan Porras

— 3 min read
Advanced Prompt Engineering for Claude in Agentic Coding Workflows
Photo by Kelly Sikkema / Unsplash

The shift from chat-based assistants to autonomous coding agents marks a significant evolution in software engineering. Leveraging Claude 3.5 Sonnet or the newer Claude 4 series within agentic loops—where the model independently navigates a codebase, executes shell commands, and iterates on fixes—requires moving beyond simple instructions.

To maintain high-velocity product engineering services, CTOs and senior architects must implement a robust Context Engineering framework that maximizes the model’s reasoning while minimizing token drift and regression.

The XML-First Architecture: Structuring Logic for High-Horizon Tasks

Claude is natively trained to handle structured delimiters. In agentic workflows, XML tags are not just a formatting preference; they serve as a machine-readable protocol that prevents the model from conflating instructions, context, and state.

Standardizing the System Prompt

A production-grade system prompt for a coding agent should compartmentalize directives using nested tags. This prevents "instruction leakage" where the model ignores constraints when faced with complex code blocks.

<system_instructions>
    <role_definition>
        You are a Senior Principal Engineer operating in a "Plan-then-Act" loop. 
        Your environment is a headless Linux container with access to a restricted toolset.
    </role_definition>
    
    <development_directives>
        1. NO HARDCODING: All solutions must be pattern-based and generic.
        2. TEST-DRIVEN: Write or update a test before implementing the logic.
        3. ROOT CAUSE ANALYSIS: Fix underlying architectural flaws, not symptoms.
    </development_directives>

    <state_management>
        Current Directory: ${cwd}
        Reference `progress.json` for task tracking across iterative calls.
    </state_management>
</system_instructions>

Context Compaction and "Just-in-Time" Context Loading

One of the most significant challenges in agentic workflows is Context Bloat. As an agent performs multiple steps, the conversation history grows, leading to increased latency and decreased reasoning accuracy.

To solve this, implement a Compaction Strategy:

  1. Stateless Iteration: Instead of one long thread, spawn a fresh Claude instance for each sub-task.
  2. State Handover: Pass a condensed TASK_STATE.md or summary tag that contains the high-level goal and current progress.
  3. JIT Tools: Rather than dumping the entire repository into context, provide tools like grep, ls -R, and read_file. Instruct the model to use a "Search-Refine-Edit" pattern.

Implementing the "Plan-Act-Review" Pattern

For complex feature development, forcing Claude into a discrete "thinking" phase before tool invocation is critical. This is often implemented using a <thinking> tag in the output schema.

Example Implementation Workflow

When a task is received, the agent must follow this specific sequence:

  1. Exploration Phase: Use ls and grep to locate relevant modules.
  2. Planning Phase: Output a <plan> block detailing the proposed changes and potential side effects.
  3. Execution Phase: Invoke edit_file or shell_execute for the actual implementation.
  4. Verification Phase: Execute a test suite and analyze results.

Functional Tool Definition (TypeScript/JSON):

{
  "name": "edit_file",
  "description": "Applies a diff-style edit to a file. Requires exact string matching for the search block.",
  "parameters": {
    "type": "object",
    "properties": {
      "filePath": { "type": "string" },
      "searchBlock": { "type": "string", "description": "The exact block of code to find." },
      "replaceBlock": { "type": "string", "description": "The new code block to insert." }
    },
    "required": ["filePath", "searchBlock", "replaceBlock"]
  }
}

Handling Regressions with "Atomic Diffs"

Agentic agents often struggle with deleting necessary code or introducing syntax errors during large file overwrites. The best practice is to enforce Atomic Diffs rather than full file rewrites.

By using targeted search-and-replace blocks, you reduce the token overhead and the probability of the model "forgetting" unrelated functions within the same file. If a verification step fails, the agent should be programmed to read the stderr, categorize the failure, and revert the specific git commit before attempting a refined fix.

Why Partner with 4Geeks?

Building and maintaining these complex AI-orchestrated systems requires deep expertise in both traditional software architecture and emerging LLM capabilities. 

4Geeks, a global leader in enterprise software and AI engineering, serves as a strategic partner for companies looking to scale their engineering throughput using these advanced methodologies. From custom AI agents to full-scale cloud engineering, 4Geeks provides the senior-level oversight necessary to implement agentic workflows safely and effectively.

Final Technical Recommendations

  • Avoid Over-Prompting: With models like Claude 3.5/4, excessive "Chain-of-Thought" guidance can lead to over-triggering of tools. Use targeted instructions like "Use [tool] only if the local directory structure is unknown."
  • Git as State: Use git logs as the authoritative source of truth for the agent's progress. It provides a natural checkpoint system that the model can query using shell tools.
  • Structured Note-Taking: Maintain a tests.json or todo.json file in the root directory. This allows the agent to self-correct by checking off completed sub-tasks.

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