Scaling Agentic AI Applications in Production

Agentic AI Development for Production

Agentic AI, which wraps large language models (LLMs) in an iterative process of improvement, is essential for enterprises to drive business processes and achieve practical applications. According to Andrew Ng, Agentic AI will dominate most of the progress in AI due to its unprecedented practical applications. A study conducted on various agentic methodologies found that while a zero-shot approach with GPT-3.5 and GPT-4 achieved about 48% and 67% accuracy, respectively, Agentic AI’s iterative looping over GPT-3.5 achieved 95.1% accuracy.

Why This Matters

The development of agentic AI applications presents unique challenges when scaling for production, including identifying agentic components, implementing, deploying, testing, and tracing these agents. Traditional software development life cycles (SDLC) do not apply to autonomous agentic AI systems, requiring a new agentic software development life cycle (ASDLC) that emphasizes not just what agents should do, but also what they must never do. The cost of failure can be significant, with a study by RisingWave identifying prompt drift as the most critical failure mode in production agent failures.

Key Insights

• Agentic AI applications achieve high accuracy with iterative looping, outperforming traditional models: 95.1% accuracy with GPT-3.5, according to a study on agentic methodologies.
• The ReAct agent pattern is effective for workflows where the agent must iteratively investigate a problem, such as database debugging.
• Tool manifests require dependency management similar to software packages, as tool additions or modifications can fundamentally alter agent capabilities.
• The Model Context Protocol (MCP) provides standardized interfaces for agent-tool integration, ensuring versioning and consistency for agentic operational environments.

Working Example

def react_agent_loop(user_query, available_tools, max_iterations=5):
    """
    ReAct pattern: Iterative reasoning and action until goal achieved
    """
    conversation_history = []
    conversation_history.append({"role": "user", "content": user_query})
    for iteration in range(max_iterations):
        # STEP 1: Reason - LLM decides next action
        llm_response = llm_client.generate(
            messages=conversation_history,
            tools=available_tools,
            temperature=0.7
        )
        # STEP 2: Act - Execute tool if LLM chose one
        if llm_response.has_tool_call():
            tool_name = llm_response.tool_call.name
            tool_args = llm_response.tool_call.arguments
            # Execute the selected tool
            tool_result = execute_tool(tool_name, tool_args, available_tools)
            # Add tool result to conversation
            conversation_history.append({
                "role": "assistant",
                "content": None,
                "tool_calls": [llm_response.tool_call]
            })
            conversation_history.append({
                "role": "tool",
                "content": tool_result,
                "tool_call_id": llm_response.tool_call.id
            })
        # STEP 3: Observe - Check if we should continue
        if should_terminate(tool_result, user_query):
            break
        else:
            # LLM provided final answer without tool use
            return llm_response.content
    # Generate final response after all iterations
    final_response = llm_client.generate(
        messages=conversation_history + [{
            "role": "user",
            "content": "Provide final answer based on above"
        }]
    )
    return final_response.content

def should_terminate(tool_result, original_query):
    """
    Breaking condition logic - could be:
    - Explicit completion signal from LLM.
    - Confidence threshold met.
    - Error state requiring human intervention (Human in the loop)
    """
    if "COMPLETE" in tool_result:
        return True
    if "ERROR" in tool_result and "ESCALATE" in tool_result:
        return True
    return False

Practical Applications

• Use Case: JPMorgan Chase’s COiN (Contract Intelligence) system demonstrates the power of sequential document analysis, processing twelve thousand commercial credit agreements in seconds with near-zero error rates.
• Pitfall: Attempting to make everything agentic, which can lead to unnecessary complexity and decreased performance.

References:

• https://www.infoq.com/articles/prompts-to-production-playbook-for-agentic-development/
• https://www.microsoft.com/en-us/research/publication/agent-orchestration-patterns/
• https://langsmith.co/
• https://www.risingwave.com/blog/prompt-drift/