Introduction: Why an AI agent is essential today
The customer support sector is undergoing a transformation thanks to multimodal language models and new open decision-making agents. In 2026, companies that want to offer 24/7 assistance must evaluate tools such asGLiNER2.5-Decide,CLM-8BandFLUX 3 Actionto build AI agents that not only respond, but act consistently with the customer's state.
The latest models supporting the agent
1. GLiNER2.5-Decide by Fastino Labs
GLiNER2.5-Decide is a 340 million parameter, open-weight decision model that runs on standard CPUs. It accepts a text prompt and a schema of typed questions and returns structured decisions. For customer support, it is ideal for extracting entities such asproduct,problemandaction_requestedfrom each chat.
2. CLM-8B by Contrastive-LM
CLM-8B is aSystem One
3. FLUX 3 Action by Black Forest Labs
FLUX 3 Action is a 7 billion parameter open world model that interprets visual scenes and coordinates robotic actions. For remote support, it can map a screenshot of the customer's UI to an action plan, allowing the agent to guide the user step-by-step.
How to choose the right tool for your use case
- Structured extraction
- Fast action suggestion
- Visual support and interface navigation
Evaluate the complexity of your workflow: if you need to extract data from text chats, GLiNER2.5-Decide is the optimal choice. If you need to decide in milliseconds between different support actions, CLM-8B excels in speed. For scenarios that include screenshots or user interfaces, integrate FLUX 3 Action for contextual navigation.
Practical implementation: a step-by-step workflow
Capture the customer's message and normalize it into a compact format:
{
"messaggio": "My vacuum robot won't start.",
"timestamp": "2026-09-25T14:23:00Z",
"canale": "chat"
}Use a prompt like this (works on any CPU):
Prompt: "Extract entity of type [product, problem, action_requested] from the following chat:
{chat_history}"The model returns a structured JSON:
{
"prodotto": "vacuum robot",
"problema": "won't start",
"azione_richiesta": "repair"
}Take the list of possible actions (e.g. ["restart", "schedule a technician visit", "offer a refund"]) and send them to CLM-8B with the current state:
{
"stato": {"prodotto": "vacuum robot", "problema": "won't start"},
"azioni_candidati": ["restart", "schedule a technician visit", "offer a refund"]
}CLM-8B returns a score and the suggested action, for example"restart" (0.94).
If the user shares a screenshot of the device's control interface, FLUX 3 Action maps the screen to an action plan:
Prompt: "Given the screenshot of the robot's control panel, generate a sequence of robotic actions to restart it."
Result: ["press reset button", "wait 5 seconds", "check status"]Example code: complete prompt for an agent
system_prompt = """
You are an AI customer support agent. Follow this flow:
1. Extract entities (product, problem, action_requested) using GLiNER2.5-Decide.
2. Evaluate candidate actions with CLM-8B.
3. If a screenshot is present, use FLUX 3 Action to generate robotic actions.
4. Respond to the user with the recommended action and guided steps.
"""
user_input = "My vacuum robot won't start."
# Call GLiNER2.5-Decide
entities = call_model("GLiNER2.5-Decide", user_input, schema=["product","problem","action_requested"])
# Evaluate actions
state = {"product": entities.product, "problema": entities.problema}
actions = ["restart", "schedule a technician visit", "offer a refund"]
score_action = call_model("CLM-8B", {"stato": state, "azioni_candidati": actions})
# If screenshot
screenshot = get_latest_screenshot()
if screenshot:
robot_steps = call_model("FLUX 3 Action", screenshot, task="restart")
final_response = f"I suggest you {score_action.action}. {robot_steps if robot_steps else ''}"
"""Testing and optimizing the agent
- Use a benchmark set with real chat cases to measure accuracy and response time.
- Monitor false positives in entity extraction and retrain the GLiNER2.5-Decide model with labeled data.
- Implement an A/B feedback system: show the CLM-8B suggested action and collect whether the user finds it helpful.
Future trends and security considerations
The year 2026 sees the rise of collaborative agents: GLiNER2.5-Decide, CLM-8B and FLUX 3 Action are converging in orchestration platforms such asAgentFlow-X. These platforms allow models to exchange contexts, improving consistency.
From a security standpoint, it is crucial to apply data anonymization before extraction, limit access to CLM-8B APIs with service keys, and use visual sandboxes for FLUX 3 Action so that it does not expose real user interfaces.
Conclusions
Building an AI agent for customer support in 2026 is not about choosing a single model,