Complex Reasoning in Agentic AI: Enhancing Decision-Making Capabilities

Summary:

• Agentic AI combines technologies like LLMs, machine learning, computer vision, and NLP to enable perception, understanding, and learning.
• Agentic AI can autonomously collect data, reason, make decisions, and take action without needing constant human input.
• In security, agentic AI improves response speed, accuracy, and efficiency by detecting and addressing threats proactively.

Data from Star Trek. The bartender in Passengers. Ultron and Vision from Marvel.

Take an actor, add some makeup and special effects, and have them confuse some human emotions and expressions—that’s the formula for just about any android from sci-fi movies.

As audience members, we tend to accept this with very little thought. For one thing, it’s fiction, so as long as it’s done without glaring errors, we’re willing to accept a lot of things for the sake of a good story. But on a deeper level, it’s easier to accept that these androids would be so advanced in their logical capacities and human-like communication skills because those are things we ourselves are naturally born with. It’s not much of a stretch of the imagination to take what humans can easily do and add some advanced computer capabilities to the mix.

In real life, however, imitating the complex reasoning humans do every day is significantly more complicated. Most people never consider just how many components are required for even basic logic and reasoning. 

AI has exploded onto the scene over the last few years, but up until recently, it only had the capacity to react to direct commands. Agentic AI—with its ability to perceive and analyze situations and then actually act on those findings—changes the game by introducing proactive, complex reasoning with minimal human involvement required.

The intelligent decisions and actions from agentic AI allow businesses to level up their security efforts, speeding response times, improving situational awareness, and even predicting problems before they have the chance to escalate. 

Agentic AI: Upgrading AI Application Potential

Prior to agentic AI, artificial intelligence was reactive, only generating outputs when directly told to or when certain specific conditions were met. While reactive technology has incredible potential and can still be very useful, it has limits (literally very clearly defined limits), which is why agentic AI is such a significant breakthrough. 

Agentic AI wouldn’t be possible, however, without the other forms of AI that preceded it:

• Large language models (LLMs), which are deep learning models trained on incredible amounts of data. LLMs process information, identify patterns, and draw conclusions; they often power the reasoning engines in agentic AI.
• Machine learning (ML), which allows computers to use real-time feedback loops to learn and improve over time.
• Computer vision, which lets computers “see” or interpret visuals like humans.
• Natural language processing (NLP), which lets computers understand human speech, making it easy to interact with LLMs.
• Visual language models (VLMs), which combine natural language with visual data to understand and generate visual things like humans (VLMs are similar to computer vision, but are far more advanced).
• Generative AI (GenAI), which can create audio, text, and images based on LLM training data. Popular AI services like ChatGPT are classified as GenAI.

Agentic AI combines many of those technologies and goes even further, most notably acting without direct prompting. 

How Agentic AI Works

The logic and reasoning processes that humans tend to take for granted require several specific steps and components to replicate. 

In order to achieve AI-driven automation, AI systems need to be able to understand and contextualize the information around them. They then need to be able to discern which situations classify as normal, which are abnormal, and which actually merit action—not to mention being able to decide what actions are most appropriate for the situation at hand. 

There’s so much that goes into agentic reasoning to optimize it for the best outcome. The fundamental agentic AI processes include the following:

• Goal-oriented data collection
• Perception and reasoning
• Decisions and planning
• Action
• Learning

Goal-Oriented Data Collection

One of the stark contrasts between reactive systems and proactive systems is the ability to seek out and identify problems autonomously. A big part of this is the ability to analyze multiple sources of information (including text, code, video feeds, and sensors) in real time. 

Another large component is goal-directed behavior, meaning that the system is working toward a specific outcome (such as keeping a property secure). Without a goal, agentic AI wouldn’t need to reason, act, or learn.

Perception & Reasoning

Once it has collected data, the AI processes it to detect patterns, understand context, and perceive abnormalities. Agentic AI uses NLP, computer vision, and other capabilities to sort through the data, then uses its reasoning engine, which is typically powered by an LLM, to make sense of the information. 

After identifying anomalies, the AI has to determine if this abnormality is a threat, and if so, what to do about it. To do this, agentic agents will often break complex problems into smaller tasks to allow the system to tackle the problem in manageable pieces.

There are several approaches to agentic reasoning, including:

• Conditional logic, which means the agent carries out an action when a corresponding condition is met.
• Heuristics, which has more of a trial-and-error approach.
• ReAct (Reason + Act), which follows a think-act-observe loop.
• ReWOO (Reasoning Without Observation), which breaks tasks into subtasks, uses tools to add evidence and facts, and draws conclusions.

Decisions & Planning

Before it can take action, agentic AI evaluates possible actions with the corresponding situations in order to choose the most efficient and effective outcome possible. To do this, it must consider and evaluate trade-offs and options based on its training dataset, memory, and any other external tools at its disposal. It can also adjust as needed if situations escalate or deviate from the expected outcome.

Action

The ability to act is what makes agentic AI truly stand apart from its predecessors. However, the actions the agentic AI can take are dependent on what tools it is connected to. 

In a security situation, these actions could include the following:

• Accessing camera feeds to verify suspicious activity
• Triggering door locks or targeted flood lights
• Delivering custom audio warnings
• Sounding alarms
• Reporting to the right people as needed, potentially advising on next-step actions

Learning

AI’s reasoning and capabilities will only continue to improve with time, but that is only possible because of the learning that takes place after each action. Without learning, AI remains static. 

Feedback after action can be given directly by human evaluators or generated by the AI itself with human oversight. Different systems use different types of learning tools, with reinforcement learning being one of the most common.

Reinforcement learning gives its feedback as rewards or penalties, mimicking how humans learn. For example, when a toddler is learning to walk, certain positions and steps may cause the toddler to fall (which is a penalty) while others keep the toddler upright and balanced (which is a reward). Instead of physically feeling the effects of reinforcement learning, the AI receives its penalties and rewards as numerical values designed into the system.

As the AI receives feedback, it can identify areas for improvement across the entire process, allowing it to implement the learned information in future situations. Even if the AI is meant to self-evaluate, it is important for humans to continuously monitor the system to ensure things stay on track. 

This analysis and understanding can also lead to the ability for predictive analytics, allowing it to flag behaviors that have historically led to other problem situations. 

Enhancing Security with Agentic AI

The proactive and automatic nature of agentic AI is changing the game for the security industry. Agentic AI allows for:

• Faster, more efficient responses
• Fewer false alarms
• Enhanced threat deterrence and detection
• Increased situational awareness
• Optimized resource allocation

Security is faster, smarter, and safer with the intelligent application of agentic AI, which is why LiveView Technologies® now offers agentic AI capabilities in conjunction with its mobile security units. For more information, contact LVT for a demonstration today.