🟢 How modern agentic AI systems adapt is proposed?
What is agentic AI:
These are large models that can:
- invoke tools,
- use memory,
- perform tasks in several steps.
What is adaptation:
Any change to the agent or its tools based on feedback, from code verification to human evaluations.
4 types of adaptation:
A1 - Agent Adaptation from Tool Execution
The agent is updated based on what happened during the execution of tools: the code either ran or crashed, the search either found something or not.
A2 — Agent Adaptation from Output Evaluation
The agent is updated based on evaluations of the quality of its final actions: human feedback, automatic checks of answers, the quality of plans.
T1 - Tool Adaptation Independent of Agent
The tools are trained separately, and the agent remains "frozen". For example, a pre-trained retriever or a code searcher.
T2 - Tool Adaptation from Agent Signals
The agent remains fixed, but the tools adapt to its behavior - which documents really helped, which hints improved the task execution.
Why this is important:
- For the first time, the work systematically organizes the methods of adaptation of agentic systems.
- Helps to understand the trade-offs: the cost of training, flexibility, portability, modular updates.
- Shows the history of the development of methods A1, A2 and T2, how they became more complex and what signals they began to use.
The view boils down to two axes:
- you can change the agent,
- you can change the tools,
- and the data and feedback serve as fuel for both strategies.
This taxonomy helps to see the connections between dozens of modern works and to understand where the new generation of agentic architectures is heading. (PDF on GitHub)
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