Rethinking AI Agent Deployment: Start with Job Design, Not Just Technology

As companies accelerate their adoption of AI, many organizations are leveraging frameworks such as MCP (Model Control Platform) and A2A (Agent-to-Agent) from major LLM vendors. These tools support AI agent collaboration and contextual understanding, but are fundamentally LLM-centric rather than business-centric.

To succeed with AI implementation, reverse the structure: design the “job” first, then select the optimal AI agent for that role.

Limitations of LLM-Centric Design: The Risks of Treating AI as “General-Purpose Workers”

Current MCP and A2A frameworks treat AI agents as “general-purpose workers” who can do anything if given the right tools and instructions. Real businesses, however, define roles, provide training, and standardize processes for continuity.

• According to Cognixia, MCP emphasizes structured, linear processing, while A2A aims for flexible, collaborative intelligence. Both lack standardized “job definitions,” making them vulnerable in real-world operations.

Vendor Lock-In Risks: Structural Issues That Undermine Flexibility

LLM providers optimize their ecosystems for their own models, making prompt design and tool integration difficult to migrate. Switching models requires major reconstruction, leading to “prompt debt” and “vendor lock-in.”

• DEV Community highlights that such lock-in is a major challenge in LLMOps. Standardization and portability are essential for flexibility and scalability.

AI Agents Need “Job Design” Too: The Same Approach as Talent Acquisition

To build reliable and scalable AI systems, treat AI agents as “employees.” Define the job, then select the agent best suited for it.

• Deloitte research shows that domain-driven job design is necessary for AI agents, including access control, self-assessment, and human oversight.

A Three-Step Framework for Enterprise AI Implementation

1. Job-Specific Benchmarking (Interview Guidelines)

   • Clearly define the job: tasks, required skills.
   • Create benchmarks simulating real business scenarios to evaluate candidate LLMs.

2. Talent Development (MCP Tools, RAG, Fine-Tuning, etc.)

   • Train selected agents with relevant business knowledge:
     • Tool integration via MCP
     • Knowledge supplementation via RAG (Retrieval-Augmented Generation)
     • Business adaptation via fine-tuning and prompt design

3. Performance Evaluation (Validation in Real Business)

   • Operate agents in real environments and assess KPI achievement and exception handling.
   • Recommended metrics: latency, cost, token usage, accuracy, robustness, adaptability.
   • Combine automated evaluation with human feedback; requires AI development expertise.

Interoperability Challenges: Fragmented AI Ecosystems

Protocols like MCP, A2A, ACP, and ANP are emerging, but true interoperability between different LLMs is still unrealized. Differences in standards and context processing hinder modular, reusable AI systems.

• arXiv research recommends starting with MCP and gradually transitioning to distributed protocols like ANP.

Conclusion: AI Agents Are “Employees,” Not “Magic Wands”

AI agents won’t deliver results just by being implemented. Only through job definition, training, and evaluation can they become “digital employees” who truly contribute to the enterprise.