AI Agents are the next wave of the Generative AI wave that was triggered by the launch of ChatGPT almost two years back.
Starting from writing stories to telling jokes, we have seen the launch of AI Assistants and Copilots for a variety of use cases including enterprise ones. Large Language Models have moved along to handling image, voice and videos - truly multimodal capabilities. Newer models are getting better at multi-step reasoning and solving mathematical problems with higher accuracy. This is helping these AI models to develop capabilities of ‘Agency’ and ‘Autonomy’.
Let’s understand these definitions and that will help us relate to Agentic Software that is coming our way as the next wave of Generative AI.
‘Agency’ and ‘Autonomy’ define the key nature of Agentic systems. As per Google’s Generative AI the definitions are as follows:
Agency
The ability to take action or choose what action to take. It can also refer to the level of control someone has over their work environment.
Autonomy
The freedom to make decisions, take risks, and exercise judgment without constant supervision. It can also refer to the degree to which an intelligent entity can set goals, make decisions, and take actions without the approval of any other intelligent entity.
A truly intelligent system can develop Agency and Autonomy - these are very powerful capabilities for a machine based system to inherit and develop further.
What is Goal Based Agent in AI?
An Agentic system can be assigned a goal e.g. a business goal and can be minimally supervised or completely unsupervised to perform a series of sequential and parallel tasks iteratively to achieve the given goal within a set of defined environmental constraints.
These agents will have access to information, derive knowledge from information, use reasoning capabilities of LLMs for task decomposition and decision making, tap into their short term and long term memory to carry out a set of discrete actions.
Sounds familiar? This is the same way humans perform complex tasks.
Importance of Goal Based Agent in AI
A goal-based agent is a type of intelligent agent in AI that operates by taking actions to achieve specific objectives or goals. It is designed to act autonomously in dynamic environments by making decisions based on predefined goals rather than just following a set of rules. Here is why goal-based agents are important in AI:
1. Improved Decision-Making
Goal-based agents focus on achieving specific outcomes, allowing them to evaluate different actions based on their effectiveness in reaching a goal. This enables the agent to make more informed and optimized decisions in uncertain or dynamic environments.
2. Flexibility and Adaptability
Unlike reflex-based agents that respond only to immediate stimuli, goal-based agents can adapt their behavior to changing circumstances. They can plan multiple steps ahead and adapt their plans if the environment changes, making them suitable for real-world applications where conditions are often unpredictable.
3. Capability for Complex Tasks
Goal-based agents can handle complex and multi-step tasks because they can decompose a larger goal into smaller sub-goals. This makes them highly effective in environments that require long-term planning, such as robotics, autonomous driving, and strategic games.
4. Learning and Improvement
By incorporating machine learning algorithms, goal-based agents can learn from past experiences and improve their decision-making strategies over time. They can adjust their approach based on what actions have been successful or unsuccessful in achieving their goals.
5. Human-Like Reasoning
Goal-based agents often mimic human-like reasoning by setting and achieving goals, making them more intuitive and relatable for human users. This is particularly useful in AI applications like virtual assistants, where natural interaction and goal-oriented behavior are critical.
6. Optimization in Resource Management
These agents are capable of optimizing resources by determining the best path to achieve their goals with minimal effort, time, or cost. This is valuable in logistics, supply chain management, and automated customer support, where efficient resource allocation is crucial.
7. Enhanced Automation
By focusing on goals, these agents enable higher levels of automation in systems. They can autonomously handle tasks that would otherwise require human intervention, freeing up human resources for more strategic activities.
8. Scalability Across Domains
Goal-based agents can be applied across various domains and industries. Whether in healthcare, finance, education, or customer service, their goal-oriented nature allows them to be customized to meet specific industry needs and challenges.
How does Goal Based Agent in AI work?
Agentic Architecture
The above diagram is a high level representation of how the Agentic Software works. The core of the system is the central processing unit which happens to be a Large Multimodal Model (LMM) or Large Language Model (LLM). It should have the necessary capability to understand human language instructions or prompts, convert that into a goal based problem and outline a series of sequential or parallel steps to arrive at a solution. Embedded instructions will act as guidance to operate within a set of defined constraints to avoid unnecessary outcomes. The model will have access to external memory to retain the context of reasoning, thinking, actions and conversations undertaken as part of the problem solving process.
It will also have access to external tools that will aid in carrying out necessary actions at each step to take it closer to the defined goal.
Applications of Goal Based Agents in AI
A goal based agent in AI model can also observe its decisions and actions and do self reflection to determine if it is on the correct path to solve the problem. If it determines that there are deviations, it can also self correct its decisions and actions to do a course correction. Such capabilities in machines will enable us to unleash the wave of ‘intelligent automation.’
Automation so far has been rule based, static hard coded instructions and uses direct human intelligence to accelerate the performance of tasks. This has helped in enabling businesses and enterprises to increase productivity and efficiency to serve customers better.
With intelligent automation we can take that efficiency to the next level by having machines inherit derived intelligence of humans and using their vast computation power to accelerate decision making in a semi automated manner or fully automated manner.
How Engati Utilizes Goal-Based Agents in AI
As we start to develop such smart systems we have to place appropriate guardrails to ensure the intelligent autonomous systems conform to human standards and values and do no harm.
At Engati we have embarked upon utilizing the power of Generative AI and AI Agents to build intelligent applications that are outcome based to help our customers solve complex problems and achieve their business goals. We have developed the domain understanding and are imparting that knowledge to our applications. We foresee a future where we will deploy AI Agents in public cloud, private cloud and On Premise data centers as per customer requirement and bring on this technology to solve practical problems which improve the topline and bottomline performance of businesses. In a series of upcoming articles we will outline how we are going about achieving this.
Real-World Use Cases of Goal Based Agents in AI
Goal based AI agents will serve as a valuable resource for Marketing and Sales teams with specific focus on Lead Capture, Lead Qualification, Lead Scoring, CRM integration, Nurture, Engagement and Conversion (leads and existing customers for Nurture, Engagement and Conversion).
Agents will be able to answer queries via content available across the website as well as from all the data fed to them using Natural Language Processing (NLP). This will help them provide hyper personalized suggestions and recommendations on products and user queries. Goal Based AI Agents will also be able to perform simple tasks -
- Book an appointment,
- Perform a checkout,
- Add leads to CRM system,
- Retrieve lead information from CRM to segment and create personalized messaging,
- Generate personalized content for marketing campaigns for better engagement
Benefits of Engati’s Goal Based Agents in AI
At Engati, our focus is to assist businesses in revenue growth by personalizing user experience, assisted conversions. automating simple and repeated business workflows, reducing cost and time to convert, handling worldwide customers and most importantly adhering to performance goals set by businesses for engagement and conversion.
Challenges and Limitations of Goal Based Agents in AI
One of the primary challenges in developing autonomous AI agents is that the technology is not yet fully mature. As a result, these agents will initially be semi-autonomous, requiring controls and guardrails to prevent deviations from their intended goals and minimize errors. Achieving fully autonomous AI agents capable of handling simple business processes may still take several months. Furthermore, the limitations in current large language models (LLMs), particularly in planning and reasoning, make it necessary to keep a human in the loop. This reliance on human oversight is a significant hurdle to realizing fully autonomous AI agents in complex environments.
