Introduction: Why an AI agent is the solution for modern business workflows
In 2026, the evolution of models such as Thinking Machines' Inkling 2 Small, Baidu's ERNIE 5.5 Flash Base, and tools like the Together Compiler is making agents faster, cheaper, and easier to integrate than ever before.
What are the best AI agents for business workflows in 2026
1. Agents based on sparsely activated MoE models
- Why they work:Models like Inkling 2 Small (594B MoE with only 17B active) deliver high performance with low power consumption.
- Use case:Predictive analytics, customer support, and content generation.
2. Agents optimized with Together Compiler
- What it offers:
- Use case:Real-time data pipelines, anomaly detection, and performance monitoring.
3. Lightweight agents based on ERNIE 5.5 Flash Base
- Strengths:A 280B MoE base model with only 14B active, ideal for edge devices and low-cost SaaS services.
- Use case:Support assistants, document routing, and robust process automation.
How to build an agent for your workflow: a step-by-step example
Below is a practical example that combines an agent orchestrator with a lightweight MoE model (e.g., ERNIE 5.5 Flash Base) using the open-source LangChain library.
Step 1: Set up the agent
import langchain
from langchain.chat_agent import create_chat_agent
from langchain.llm import LLMWrapper
# Initialize the model (replace with your actual endpoint)
llm = LLMWrapper(model_name="ERNIE-5.5-Flash-Base", temperature=0.2)
# Define the agent's role
agent = create_chat_agent(
llm=llm,
tools=[...], # add your tools (search, DB, etc.)
system_prompt="You are a dedicated assistant for business workflow automation. Analyze data, initiate approved actions, and concisely report results."
)Step 2: Define a simple workflow rule
- Input:
- Output:Report + database modification.
Step 3: Test and iterate
Use the LangChain testing framework to verify that the agent complies with corporate policies and produces accurate results.
Current trends: what's happening in November 2026
Together AI releases Together Compiler
Thinking Machines launches Inkling 2 Small
With 594B MoE parameters and only 17B active, Inkling 2 Small sets new standards for efficiency. It is particularly suitable for businesses that need high computing power without high energy consumption.
Baidu introduces ERNIE 5.5 Flash Base
This 280B base model with 14B active is now available for edge agent development, enabling businesses to bring AI closer to the data.
Practical takeaways and next steps
- Assess your requirements:Data size, budget, and latency determine whether to choose a heavy or lightweight agent.
- Leverage new tools:Integrate Together Compiler for automatic optimization; use ERNIE 5.5 Flash Base for edge inference.
- Experiment with open-source code:Start with LangChain and a lightweight MoE model to create your first agent.
- Monitor performance:Implement agent tracking and policy control to ensure compliance and ROI.
Conclusion: Your next step toward intelligent automation
Start with a small proof-of-concept, measure results, and scale the agent across the organization. The future of business workflows is already here.