In the last two segments of this series, I have already presented the subject matter of Agentic AI and how this is different from conventional AI systems. We shall go a step further today.
If the future is indeed Agentic AI, how do we go about designing systems to function in this way?
What makes an AI system not only intelligent—but autonomous, adaptive, and goal-oriented? The answer is in the architecture, learning algorithms, and the processes of decision making that we implement in such systems.
The Core Design Philosophy: Embodied Autonomy
Agentic AI requires to be developed with a value philosophy of decision agency. That is, not predicting the outcome but determining actions—sometimes independent of user input. This is not from rule-based reasoning. It requires a mix of:
System Components of Agentic AI
Let’s break this down to five underlying layers:
- Data from the external world (image, text, data feeds, APIs)
- NLP/NLU Models for Linguistic Meaning
- Situational awareness from external cues
- Internal representation of the external world
- Forecasting how outcomes are influenced by actions
- Periodically updated via feedback loops
- Embraces big goals, often vague or conceptual
- Breaks down goals into sub-goals or tasks
- Priorities dynamically depending on urgency, relevance, or feedback
- Path finding: How to get from current to goal
- Multi-step reasoning
- Tool selection and orchestration
- Carries out plans via action (internal or external)
- Engages with APIs, users, or systems
- Tracks outcomes, reroutes work if off-target
From LLMs to Agents: The Critical Turning Point
Large Language Models like GPT-4.5 as well as Claude are highly capable but agentic systems require more sophisticated orchestration than completion prediction. They include:
- Long-term memory (compared to short-term context)
- Goal-tracking systems
- Statefulness: What took place before
- Incorporating feedback: Iterating and retry
Frameworks like LangChain, AutoGPT, OpenAgents, etc. try to provide these capabilities as meta-layers on top of LLMs. They are just early efforts.
That is what would make a clever chatbot an authentic agentic system.
The Real Problem: Getting AI to “Stick the Landing”
You can instruct an AI to plan. You can instruct it to act. But acquiring it to:
- Understand trade-offs
- Manage uncertainty
- Rebound from failure
- Evolve over time
— that is where the true engineering is done. That is what would make a clever chatbot an authentic agentic system.
Design Patterns in the Wild
The following are three things I notice in daily design of agentic systems:
Agents that re-evaluate their plans using a separate model that critiques output quality—enabling self-correction at each step.
Agents divide the work into subtasks, parallelize them, and report back results prior to proceeding—maximizing throughput without losing coherence.
Vector databases (e.g., Pinecone, Weaviate) and other tools are used to save and recall previous context, enabling long-term consistency across sessions.
Final Thoughts
Designing Agentic AI is a question of building systems to make decisions—and to improve at decisions over time. Not an easy jump from predicting to deciding, from acting to adapting. But a jump that must be taken. With this new era of autonomous intelligence coming to the forefront, the solution is not more power or more data, but more intention. See Part 4, where we discuss how to concretely build these systems.