Adaptive Agents on AIBussin — AI applications, systems and books

Advanced Agents From First Principles 14: Can Your Agent Learn From Its Own Trajectories Without Learning the Wrong Lessons?

Sun, 09 Aug 2026 10:33:00 +0100

An advanced agent now leaves behind something extremely valuable:

evidence.

Not merely chat history.

Not merely model outputs.

Not merely traces.

A sufficiently instrumented system can record:

what state it was in,
what alternatives it considered,
which route it selected,
what branches it pruned,
which model or specialist it escalated to,
which tools it called,
which critic changed the answer,
what verification evidence was produced,
how much compute was spent,
and whether the final result actually passed.

That immediately suggests a tempting idea:

Advanced Agents From First Principles 08: Is Your Agent Spending the Same Compute on Every Task? Build Adaptive Agents That Escalate Only When Needed

Sun, 09 Aug 2026 00:00:00 +0000

Build adaptive agent runtimes that start cheap, measure uncertainty and failure, and escalate selectively into deeper reasoning, more samples, search, stronger models or specialist review only when the evidence justifies it.

Advanced Agents From First Principles 09: Can Your Agent Actually Learn From Previous Runs?

Sun, 09 Aug 2026 00:19:00 +0100

Can Your Agent Actually Learn From Previous Runs?

A production agent can execute the same class of task hundreds or thousands of times.

It can see the same failure repeatedly.

It can discover the same workaround repeatedly.

It can call the same expensive model repeatedly.

And still behave as if every task is the first one it has ever seen.

That is not necessarily a memory problem.

It may already have excellent memory.

Intelligence Through Execution: The Executable Cognitive Kernel

Tue, 10 Mar 2026 21:58:14 +0000

🧭 Summary

Most modern AI systems treat intelligence as something stored inside a model.

A neural network is trained on massive datasets, its weights are adjusted, and those weights become the system’s knowledge. When the model produces an output, we interpret that output as the result of the intelligence encoded inside those parameters.

But this perspective has a limitation.

Once training is complete, the model is largely static. It does not improve through its own actions, and it does not adapt based on the outcome of its behavior unless we retrain it.

Compiling Thought: Building a Prompt Compiler for Self-Improving AI

Thu, 26 Jun 2025 13:31:41 +0100

How to design a pipeline that turns vague goals into smart prompts

🧪 Summary

Why spend hours engineering prompts when AI can optimize its own instructions. This blog post introduces a novel approach toward creating a self-improving AI by treating prompts as programs. Traditional AI systems often rely on static instructions rigid and limited in adaptability. Here, we present a different perspective: viewing the Large Language Model (LLM) as a prompt compiler capable of dynamically transforming raw instructions into optimized prompts through iterative cycles of decomposition, evaluation, and intelligent reassembly.

Document Intelligence: Turning Documents into Structured Knowledge

Tue, 17 Jun 2025 23:31:13 +0100

📖 Summary

Imagine drowning in a sea of research papers, each holding a fragment of the knowledge you need for your next breakthrough. How does an AI system, striving for self-improvement, navigate this information overload to find precisely what it needs? This is the core challenge our Document Intelligence pipeline addresses, transforming chaotic documents into organized, searchable knowledge.

In this post we combine insights from Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers and Domain2Vec: Vectorizing Datasets to Find the Optimal Data Mixture without Training to build an AI document profiler that transforms unstructured papers into structured, searchable knowledge graphs.