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Agentic AI: Autonomous Decision-Making thanks to RLHF

Use the strengths of multi-modal AI agents by applying ethical and high-quality data labeling along with our experience in Human-in-the-Loop (HITL) technics and ecosystem.

AI agents are independent systems that understand their surroundings, make choices, and take action to reach their goals. They do this accurately, clearly, and in a way that matches what people expect. Unlike standard generative models, they tackle difficult problems and handle different types of data, making them significant advancements in automation.

At isahit, we help you building the perfect AI agent for your industry.
Here’s how:

What Are AI Agents?

AI agents are smart systems that sense, analyze, and act on your data and information based on  their environment. Unlike traditional generative AI, these agents tackle complex, multi-step tasks on their own. They can process different data types like text, images, or PDFs for instance, making them more versatile in solving problems.

How our HITL How AI Agents Work?
AI agents follow a three-step process:

1.
Perception

The AI agent receives information from its environment via physical or virtual sensors.

2.
Decision-Making

Using APIs, automation platforms, large language models (LLMs), and IoT sensors, the AI Agent analyzes the input data and determines the appropriate response.

3.
Action

They execute their decisions by acting on the environment through digital commands (software) or physical movements (hardware, robots).

What are the different forms of AI Agents?

They come in various forms:
→ Reflex Agents: React immediately using preset rules.
→ Learning Agents: Get smarter over time with experience and continuous training.
→ Interactive Agents: Communicate and collaborate with users or other AI Agents.
→ Single vs. Multi-Agent Systems: Operate individually or work together to manage complex workflows.
→ Human-Machine Agents: Enhance decision-making by integrating AI assistance in human workflows (e.g., customer support chatbots).

How our HITL & RLHF workflows enhance AI Agents?

We empower your next AI agents with real-world context by collecting diverse, multi-modal datasets and using expert annotation across text, vision, and audio. Our process relies on Human-in-the-Loop (HITL) and Reinforcement Learning from Human Feedback (RLHF) to refine the Agentic AI produced.
Our team evaluates and adjusts the AI agents, setting clear decision documents to ensure that tasks are either handled automatically or given to human support when necessary.

Our Agentic AI Development Process

At isahit, our strong Human-in-the-Loop approach makes sure our AI agents perform at their best. Here’s how: developing a high-performing AI agent is done following these steps:

• Define the Objective:
Determine if the agent’s purpose is automation, data collection, or user interaction.
• Choose the Environment:
Decide whether the agent will operate in a virtual space, a real-world setting, or a hybrid environment.
• Data Collection:
Gather relevant, diverse, and high-quality datasets that provide the context needed for learning.
• Algorithm & Training:
Select the best learning approach and fine-tune the model. Train the agent with the collected data and fine-tune its parameters for optimal performance.
• Testing & Monitoring:
Rigorously test the agent across various scenarios to evaluate effectiveness before deployment. And continuously monitor the agent to adapt to changing conditions and maintain reliability.

Agentic AI applications across industries

AI agents are transforming industries by integrating real-time multi-modal understanding.

• Autonomous Vehicles: AI Agents make driving decisions based on live environmental data.
• Customer Service chatbots: AI agents create in real-time human-like support interactions in retail and insurance.
• Manufacturing: Machines perform repetitive tasks in factories with precision and efficiency.
• Finance: Fraud detection systems analyze complex transactional data live.
• HR & Recruitment: AI Agents automate resume screening and candidate matching.
• Marketing: Intelligent agents track consumer behavior across multiple channels.

AI Agents in Action
Ask us for more customer stories!

USE CASE : L'Oréal

Discover how L'Oréal uses our image annotation service to train their facial recognition algorithm and capitalise on the diversity of our workforce to avoid including biases in their models.

  • Use of a consensus process

  • Assigning images according to the skin type : (Indian, Asian, African, American, Caucasian)

  • Order of points

USE CASE : Airbus

Find out how Airbus uses our image annotation service to train its recognition algorithms on satellite imagery and capitalises on the flexibility of our service for mass annotations on an ad hoc basis.

  • Process of managing fluctuating image flows

  • Optimisation of the tool to handle several hundred annotations per image

  • Use of the directional bounding box with directional vector

USE CASE : Sodexo

Come and see how Sodexo uses our image annotation service to train their Food Recognition algorithm and capitalise on the diversity of our workforce to avoid bias in their models.

  • A tailor-made annotation pipeline

  • A tailor-made API

  • Specific interface for label management

Empower Your Business
with Agentic AI

At isahit, we excel in ethical data labeling and Human-in-the-Loop integration to create strong, smart and reliable AI agents. Partnering with us will improve your operations and give you free time at the end of the day to focus on your irreplaceable tasks and decisions!
Contact us to learn how our data labeling can help you deploy effective AI agents.

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