Introduction: Why AI agents are essential today
Companies are no longer just talking about “using AI for chat.” Today, AI agents drive entire workflows, from HR management to IT support, supply chain planning to data analysis. As agent orchestration becomes established on major platforms (led by Claude), the main challenge is no longer technology itself, but integrating reliable agents into existing business processes.How to create an effective AI agent: What approach should you take?
The most common question is: “Which tool should we use to automate a specific workflow?” The answer consists of three steps:- Define the desired outcome.Write a clear, measurable prompt.
- Choose the most suitable model and agent.Evaluate new MoE models like Kimi K3, DeepSeek V4 Pro, and GLM-5.2 to balance performance, cost, and licensing.
- Implement a continuous evaluation and improvement cycle.Use prompt-based evaluation schemes to monitor actual alignment.
Step 1: Write a results-oriented prompt
A well-structured prompt acts as the contract between the agent and the business workflow. Use this template:You are {role}, specialized in {domain}.
Take into account {context} and produce {output} in {format}.
Follow these rules: {rules}.
Provide only {output} without explanations.You are an HR assistant, specialized in managing employee leave requests. Take into account company policies (maximum 3 consecutive days without manager approval) and produce an approved or rejected response in JSON with fields: {"status": "approved/rejected", "comment": "string"}. Follow these rules: use a professional tone, cite remaining vacation days, provide only the JSON object.Step 2: Choose the right model for your agent
In 2026, comparing the dominant open MoE models has become crucial:- Kimi K3
- DeepSeek V4 Pro
- GLM-5.2
Evaluate based on three parameters:performance benchmark,license, andserving cost. For most business workflows, Kimi K3 is often the optimal choice, especially when combined with Claude orchestration for high-level tasks.
When to use an AI agent in a real workflow
When to use an AI agentdepends on three key factors:- Volume and repetition.High-volume tasks (data entry, email classification) are ideal candidates.
- Interdependency between decisions.When a task depends on multiple data or systems (e.g., updating inventory after an order), an agent can coordinate the logic.
- Need for scalability.Workflows that need to handle seasonal peaks benefit from agent-based automation.
Case study:An e-commerce company implemented an AI agent that automatically receives order data, verifies availability, updates inventory in the ERP, and notifies customers. The agent uses Kimi K3 for data extraction, Claude for message composition, and a prompt-based evaluation cycle to verify notification accuracy. The result: 35% reduction in order processing time and fewer manual errors.
Why agent orchestration is the winning strategy
In 2026, most organizations are moving from using a single AI platform to creating an agent-based orchestration. This approach enables:- Combining the strengths of different models (e.g., Kimi for data extraction, Claude for text generation).
- Isolating failures: a malfunctioning agent does not disrupt the entire workflow.
- Applying granular security controls based on role.
A common orchestration model uses amain controller(often based on a proprietary LLM) that delegates tasks to specialized agents. This controller also maintains acontinuous evaluation log, automatically updating the agent's autonomy level based on performance scores.
Controller example
# Pseudo-code for a basic orchestration controller
class WorkflowController:
def __init__(self, model="claude-3-opus"):
self.model = model
self.agent_registry = {}
def register_agent(self, name, agent_func):
self.agent_registry[name] = agent_func
def execute_task(self, task_description, context):
# Select the most suitable agent
selected = self.select_agent(task_description)
# Execute with evaluation
result = self.agent_registry[selected](task_description, context)
score = self.evaluate_result(result)
self.update_agent_autonomy(selected, score)
return resultPractical takeaways
- Start with a single high-impact agent.Choose a repetitive and easily definable task, such as email classification.
- Use open-source MoE models for core workloads.Start with Kimi K3 and compare metrics with DeepSeek V4 Pro and GLM-5.2.
- Implement a continuous evaluation cycle.Create an evaluation prompt that measures accuracy, tone, and policy compliance.
- Design for orchestration.Even if you start with a single agent, design a registry and delegator that can be expanded in the future.
Conclusion: The future of AI agents in business workflows
Useful resources
- MoE Models Guide: Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2 (MIT Press).