Agentic AI is like schools of fish looking for feeding grounds
Definition of Agentic AI *
Agentic AI refers to artificial intelligence systems that possess the ability to act autonomously and make decisions based on their own goals, preferences, or objectives. Unlike traditional AI, which typically follows predefined rules or responds directly to user input, agentic AI can initiate actions, adapt its behavior, and pursue complex tasks without constant human oversight. This type of AI often features advanced reasoning, planning, and self-motivation, making it capable of interacting with environments in a proactive and dynamic manner.
Definition of Artificial General Intelligence (AGI) *
Artificial General Intelligence (AGI) represents a type of AI that is capable of understanding, learning, and performing any intellectual task that a human being can. Unlike narrow AI, which specializes in specific applications, AGI demonstrates flexibility and adaptability across a broad spectrum of activities. AGI systems are designed to reason, solve problems, and transfer knowledge between different domains, closely mirroring the general cognitive abilities of humans.
Difference between Agentic AI and AGI
Agentic AI is usually autonomous and makes decisions based on defined or self-defined goals within a narrow band of specific problems. AGI can understand, learn, and apply knowledge in a wider range of contexts, leveraging broad cognitive capabilities like humans can. There is, however, a growth path for Agentic AI to learn and broaden its goal and context domains. Then the differences start to blur and overlap, creating some muddy opportunities and situations. This writing assumes that this growth path is where Agentic AI is headed
Real Agentic AI Attained: How Will You Know?
Ultimately, Agentic AI will reach the pinnacle of seven dimensions that I will describe below. View this as an arch of attributes reaching over us as we pass through underneath, with the keystone dimension being independence. All these dimensions are growing in maturity and capability with Agentic AI, simultaneously yielding real Agentic AI.
Significance
This dimension represents the guiding goals for Agentic AI as it pursues results within solid boundaries and good governance. This may mean that the goals can blend and bend on context with emergence. Eventually, goals will be adapted dynamically.
Context of Interest
The goals, static or emerging, will point to context(s) and supporting data/knowledge of interests driven internally or externally. These contextual domains will contain resources for agentic AI to leverage and potentially update, affecting other agents, building patterns of repeatability.
Detection
Agents will need to be sensitive to change, even if looking for expected events or triggers. Planning agents will also look for patterns of interest and match them to pre-established or dynamic strategic planning analysis results, looking for opportunities to make change. If emergence is detected, these agents will report and represent the patterns of conditions, thus potentially shifting outcomes.
Independence
Agents will be given more and more freedom to respond to changes detected while operating in real-time. This is where the lines between AGI and AI agents will blur. They may cooperate with each other or override each other based on situations anticipated or not. Full freedom will be where there are significant learnings to be had. This is a real step of faith as process control fades.
Continuous
Agents will be in an always-on mode in a real-time fashion and cycling through an iterative improvement cycle based on instant optimization within guidelines, boundaries, and blended goals, looking for adaptation opportunities or threats.
Collaboration
Agentic AI will not only leverage legacy-wrapped processes, snippets, code, bots, and purpose-built agents, but also collaborate with agents that are specialized, physically embedded, or highly interdependent. A brokering or management agent may interact with other agents depending on goal compatibility.
Swarm Dynamics
AI agents will not only cooperate but swarm to create dynamic success patterns of operations, adapting to emergent conditions to stay within significant goal priorities. This will dynamically change the shape of agent executions to match any emergence detected.
Net; Net:
Are we there yet? Well, we have and will continue to see Agentic AI operating on the battlefield. While this is not my favorite topic, there are also successes in IOT device behavior and supply chain situations. General business applications are early because business is busy with chatbots, automation, algorithmic optimization, LLMs, and agent interactions with processes. Expect a change from inside-out change agents/bots being controlled by processes to real outside-in Agentic AI over the coming years. From process-driven to goal-driven with guidelines and guardrails. You don’t have to wait for quantum computing, but a big boost is coming from quantum.
Additional Reading:
Goal Lifecycle
Goal Management
Attaining Stretch Goals
Agentic AI in Context
Context Savvy
Data Context
Big Data
Agentic AI Detects
Business FOMO
Event Discovery
Dark Patterns
Agentic Independence
Agentic AI and Processes
Coordinated Autonomy
Agentic AI Management
Agents Making Decisions
Decisions Without Perfect Data
Agentic AI is Always on
Real-time Scenarios
Corporate Performance
Real Time is Essential
Collaboration with Agentic AI
Clearing Chaos
Results Coordinate Agents
Agents Represent Stakeholder Interests
Agentic AI Swarms
Agents Built to Swarm
Best Agents to Swarm
Swarming to Serve Customers
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