Agency is The Key to AGI
Author: Adam BEN KHALIFA
Originally published on Towards AI.
Why are agentic workflows essential for achieving AGI
Let me ask you this, what if the path to truly smart and effective AI , the kind we call AGI, isn’t just about building one colossal, all-knowing brain? What if the real breakthrough lies not in making our models only smarter, but in making them also capable of acting, adapting, and evolving?
Well, LLMs continue to amaze us day after day, but the road to AGI demands more than raw intellect. It requires agency.
Getting Our Terms Straight: AGI, Agency, and Agentic Workflows
Before we dive in, let’s define the main concepts here:
AGI — Artificial General Intelligence:
You can see it as an AI model that can perform any intellectual task a human can. This means not just understanding language or generating images, but adapting, learning, reasoning, and acting across entirely new domains.
Agency:
The capacity of an entity to act purposefully in its environment to achieve goals. A rock has no agency; a human planning their day has plenty. For an AI, agency means it’s not just passively responding to prompts but actively pursuing objectives.
Simply put, it’s the capacity to pursue goals autonomously through planning, acting, and adapting.
Agentic Workflows:
If agency is the “what”, agentic workflows are the “how”. These are the dynamic processes and systems an AI uses to exercise its agency. Think beyond a simple input-output model.
Agentic workflows involve:
Autonomous Goal-Setting & Planning: the AI doesn’t just execute a pre-defined plan, it can formulate goals and strategize how to achieve them.
Tool Use & Orchestration: like a skilled craftsperson, it can select, combine, and utilize various “tools”to get the job done.
Memory & Learning: it remembers past actions, learns from successes and failures, and adapts its strategies over time.
Adaptation in Dynamic Environments: the real world is messy, an agentic AI can adjust its plan when encountering unexpected obstacles or new information.
It’s vital to understand the difference here:
An LLM calling a weather API is just tool use.
An agentic workflow is when an LLM, tasked with “analyzing market trends for a new product,” autonomously decides to:1) search recent financial news, 2) query a sales database, 3) use a data analysis tool to spot correlations, 4) ask a specialized forecasting model for projections, and then 5) compile a summary report, re-evaluating its approach at each step.It’s like the difference between a single musician playing one note, and a conductor leading an entire orchestra.
The Limitation of Isolated Intelligence
Let’s consider a human analogy. Imagine a brilliant engineer, a genius in their field. Now, strip away their tools: no computer, no internet for research, no pen and paper for sketching ideas or taking notes, no lab for prototyping, no colleagues to bounce ideas off. Confine them to only their thoughts. How much could they truly achieve? Their raw intellect, however vast, becomes severely handicapped when uncoupled from the ability to interact, experiment, and leverage external resources.
This “intelligence in isolation” scenario illustrates a fundamental truth: intelligence doesn’t operate in a vacuum. It thrives on interaction, tool use, and the ability to execute plans in the world. If we want AGI, we can’t just build a disembodied digital brain, we need to build something that can act.
Agentic Workflows: AI can act like us, and perhaps even better
Humans are masters of adapting their “workflows.” A painter uses different tools and processes than an engineer, who uses different methods than a chef. We intuitively understand context, choose the right approach, and even invent new methods when old ones fail.
Agentic workflows aim to achieve similar capabilities:
Contextual Flexibility: An Agentic AI could switch between “investigative journalist mode”and “creative writer mode”as needed for a complex task.
Learning by Doing: Human learning is an iterative workflow: observe, hypothesize, experiment, analyze, conclude, refine. Agentic systems can embody this, trying approaches, evaluating outcomes, and improving their strategies.
Beyond Monolithic Thought: We don’t store everything in our heads. We use notes, computers, books, and critically, we delegate tasks to others. Agentic AI can similarly leverage external knowledge bases, specialized sub-agents, and computational tools, creating a distributed, more powerful form of intelligence.
Thinking About Thinking: Humans possess meta-cognition — the ability to reflect on our own thought processes and adjust them. Agentic workflows, with their capacity for self-monitoring and re-planning, are a foundational step towards AI developing its own form of meta-cognition.
Inventing New Ways: Perhaps soon enough, an advanced agentic AI won’t just use existing tools and workflows, but identify the need for entirely new ones and even contribute to their creation. A hallmark of true general intelligence.
Not Just Helpful, But Mandatory: Why AGI Needs Agentic Workflows
These capabilities aren’t just fancy add-ons. They are arguably essential for anything we’d recognize as AGI:
Tackling Complexity: Real-world problems are messy, multifaceted, and rarely solved by a single, linear process. Agentic workflows will allow AI to break down these complex challenges into manageable sub-tasks, orchestrating diverse capabilities.
Achieving Scale: Imagine trying to manage global logistics, conduct large-scale scientific research, or personalize education for millions with a single, rigid program. Agentic systems offer the modularity and dynamic coordination needed for such scale.
Adaptability and Robustness: What happens when the data changes, a tool fails, or an assumption proves wrong? A static AI might grind to a halt. An agentic AI can adapt, re-plan, find alternative solutions, and continue pursuing its goal. It can handle the unexpected.
Resourcefulness: Like our engineer, an AGI needs to be able to identify and use the right “tool”for the job at hand, rather than trying to be a jack-of-all-trades with a single block massive model.
Surpassing Human Adaptability
The first step is for AI to achieve a human-like ability to set goals, plan, use tools, and adapt through agentic workflows. But the true promise of AGI lies in surpassing these capabilities:
Speed: Learn and adapt at speeds incomprehensible to us, iterating through problem-solving cycles in milliseconds.
Scale: Manage and orchestrate operations of immense complexity, juggling thousands of variables and “tools” simultaneously.
Novelty: Devise entirely new, perhaps counter-intuitive, workflows and solutions to problems that humans haven’t even conceived of.
Self-Improvement of Workflows: An AGI that doesn’t just use workflows but actively refines, optimizes, and even discovers fundamentally new and more efficient ways to achieve its goals.
Deeper Meta-Learning: Learning how to learn, plan, and strategize more effectively over time, becoming increasingly more intelligent and capable.
Long-Horizon Reasoning: Successfully breaking down and navigating extremely complex, multi-stage goals that unfold over extended periods, adapting robustly along the way.
Obviously, this is easier said than done. Building true AGI presents formidable challenges: How do we design systems that can reliably plan in open-ended environments? How can they discover and integrate new tools seamlessly? How does the system learn which part of a long, complex workflow was responsible for success or failure?
These are active areas of research, pushing the boundaries of what AI can do. Thankfully we are witnessing more and more breakthroughs everyday, and RL — Reinforcement Learning based approaches are showing great promise.
Conclusion: Agency as the Cornerstone of AGI
The quest for AGI is more than a race for larger models or faster processing. It’s a quest for intelligence that is versatile, adaptive, and purposeful. Agentic workflows provide the framework for such intelligence, enabling AI to move beyond mere pattern recognition to become an active participant in the problem-solving process.
Just as human collective general intelligence emerged not merely from neurons, but from networks of thought, culture, and action — we must build AGI not as a single-block model, but as an AI capable of learning, adapting, and acting. Agency, in this light, isn’t just a feature; it’s the fundamental engine that will drive us towards true Artificial General Intelligence.
If you liked this article, make sure to follow for more.And you can find me on:
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
#agency #key #agi
Agency is The Key to AGI
Author: Adam BEN KHALIFA
Originally published on Towards AI.
Why are agentic workflows essential for achieving AGI
Let me ask you this, what if the path to truly smart and effective AI , the kind we call AGI, isn’t just about building one colossal, all-knowing brain? What if the real breakthrough lies not in making our models only smarter, but in making them also capable of acting, adapting, and evolving?
Well, LLMs continue to amaze us day after day, but the road to AGI demands more than raw intellect. It requires agency.
Getting Our Terms Straight: AGI, Agency, and Agentic Workflows
Before we dive in, let’s define the main concepts here:
AGI — Artificial General Intelligence:
You can see it as an AI model that can perform any intellectual task a human can. This means not just understanding language or generating images, but adapting, learning, reasoning, and acting across entirely new domains.
Agency:
The capacity of an entity to act purposefully in its environment to achieve goals. A rock has no agency; a human planning their day has plenty. For an AI, agency means it’s not just passively responding to prompts but actively pursuing objectives.
Simply put, it’s the capacity to pursue goals autonomously through planning, acting, and adapting.
Agentic Workflows:
If agency is the “what”, agentic workflows are the “how”. These are the dynamic processes and systems an AI uses to exercise its agency. Think beyond a simple input-output model.
Agentic workflows involve:
Autonomous Goal-Setting & Planning: the AI doesn’t just execute a pre-defined plan, it can formulate goals and strategize how to achieve them.
Tool Use & Orchestration: like a skilled craftsperson, it can select, combine, and utilize various “tools”to get the job done.
Memory & Learning: it remembers past actions, learns from successes and failures, and adapts its strategies over time.
Adaptation in Dynamic Environments: the real world is messy, an agentic AI can adjust its plan when encountering unexpected obstacles or new information.
It’s vital to understand the difference here:
An LLM calling a weather API is just tool use.
An agentic workflow is when an LLM, tasked with “analyzing market trends for a new product,” autonomously decides to:1) search recent financial news, 2) query a sales database, 3) use a data analysis tool to spot correlations, 4) ask a specialized forecasting model for projections, and then 5) compile a summary report, re-evaluating its approach at each step.It’s like the difference between a single musician playing one note, and a conductor leading an entire orchestra.
The Limitation of Isolated Intelligence
Let’s consider a human analogy. Imagine a brilliant engineer, a genius in their field. Now, strip away their tools: no computer, no internet for research, no pen and paper for sketching ideas or taking notes, no lab for prototyping, no colleagues to bounce ideas off. Confine them to only their thoughts. How much could they truly achieve? Their raw intellect, however vast, becomes severely handicapped when uncoupled from the ability to interact, experiment, and leverage external resources.
This “intelligence in isolation” scenario illustrates a fundamental truth: intelligence doesn’t operate in a vacuum. It thrives on interaction, tool use, and the ability to execute plans in the world. If we want AGI, we can’t just build a disembodied digital brain, we need to build something that can act.
Agentic Workflows: AI can act like us, and perhaps even better
Humans are masters of adapting their “workflows.” A painter uses different tools and processes than an engineer, who uses different methods than a chef. We intuitively understand context, choose the right approach, and even invent new methods when old ones fail.
Agentic workflows aim to achieve similar capabilities:
Contextual Flexibility: An Agentic AI could switch between “investigative journalist mode”and “creative writer mode”as needed for a complex task.
Learning by Doing: Human learning is an iterative workflow: observe, hypothesize, experiment, analyze, conclude, refine. Agentic systems can embody this, trying approaches, evaluating outcomes, and improving their strategies.
Beyond Monolithic Thought: We don’t store everything in our heads. We use notes, computers, books, and critically, we delegate tasks to others. Agentic AI can similarly leverage external knowledge bases, specialized sub-agents, and computational tools, creating a distributed, more powerful form of intelligence.
Thinking About Thinking: Humans possess meta-cognition — the ability to reflect on our own thought processes and adjust them. Agentic workflows, with their capacity for self-monitoring and re-planning, are a foundational step towards AI developing its own form of meta-cognition.
Inventing New Ways: Perhaps soon enough, an advanced agentic AI won’t just use existing tools and workflows, but identify the need for entirely new ones and even contribute to their creation. A hallmark of true general intelligence.
Not Just Helpful, But Mandatory: Why AGI Needs Agentic Workflows
These capabilities aren’t just fancy add-ons. They are arguably essential for anything we’d recognize as AGI:
Tackling Complexity: Real-world problems are messy, multifaceted, and rarely solved by a single, linear process. Agentic workflows will allow AI to break down these complex challenges into manageable sub-tasks, orchestrating diverse capabilities.
Achieving Scale: Imagine trying to manage global logistics, conduct large-scale scientific research, or personalize education for millions with a single, rigid program. Agentic systems offer the modularity and dynamic coordination needed for such scale.
Adaptability and Robustness: What happens when the data changes, a tool fails, or an assumption proves wrong? A static AI might grind to a halt. An agentic AI can adapt, re-plan, find alternative solutions, and continue pursuing its goal. It can handle the unexpected.
Resourcefulness: Like our engineer, an AGI needs to be able to identify and use the right “tool”for the job at hand, rather than trying to be a jack-of-all-trades with a single block massive model.
Surpassing Human Adaptability
The first step is for AI to achieve a human-like ability to set goals, plan, use tools, and adapt through agentic workflows. But the true promise of AGI lies in surpassing these capabilities:
Speed: Learn and adapt at speeds incomprehensible to us, iterating through problem-solving cycles in milliseconds.
Scale: Manage and orchestrate operations of immense complexity, juggling thousands of variables and “tools” simultaneously.
Novelty: Devise entirely new, perhaps counter-intuitive, workflows and solutions to problems that humans haven’t even conceived of.
Self-Improvement of Workflows: An AGI that doesn’t just use workflows but actively refines, optimizes, and even discovers fundamentally new and more efficient ways to achieve its goals.
Deeper Meta-Learning: Learning how to learn, plan, and strategize more effectively over time, becoming increasingly more intelligent and capable.
Long-Horizon Reasoning: Successfully breaking down and navigating extremely complex, multi-stage goals that unfold over extended periods, adapting robustly along the way.
Obviously, this is easier said than done. Building true AGI presents formidable challenges: How do we design systems that can reliably plan in open-ended environments? How can they discover and integrate new tools seamlessly? How does the system learn which part of a long, complex workflow was responsible for success or failure?
These are active areas of research, pushing the boundaries of what AI can do. Thankfully we are witnessing more and more breakthroughs everyday, and RL — Reinforcement Learning based approaches are showing great promise.
Conclusion: Agency as the Cornerstone of AGI
The quest for AGI is more than a race for larger models or faster processing. It’s a quest for intelligence that is versatile, adaptive, and purposeful. Agentic workflows provide the framework for such intelligence, enabling AI to move beyond mere pattern recognition to become an active participant in the problem-solving process.
Just as human collective general intelligence emerged not merely from neurons, but from networks of thought, culture, and action — we must build AGI not as a single-block model, but as an AI capable of learning, adapting, and acting. Agency, in this light, isn’t just a feature; it’s the fundamental engine that will drive us towards true Artificial General Intelligence.
If you liked this article, make sure to follow for more.And you can find me on:
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
#agency #key #agi
·72 Vue