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Paul Agapow, profile picture
Uploaded byPaul Agapow
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Agentic AI: from neurons to autonomous systems

A talk to the Data Science section of the PSI in 2026, serving as a gentle introduction to Agentic AI. It shows how neural nets, attention, transformers, and scale plus RLHF produced modern language models. It then unpacks what makes AI agentic: the observe-think-act loop, the full stack of tools, memory, orchestration and evaluation, and how multi-agent systems divide and conquer complex tasks. The back half covers where agentic AI earns its keep — high-volume expert work, document generation and synthesis — before honestly cataloguing the failure modes: hallucination, prompt injection, and more. It closes with practical guidance for data scientists on where to start, what to build first, and how to think about this as a discipline rather than a feature.

Data & Analytics◦
Related topics:
Generative AI• Agentic AI•Large Language Models•Data Science Insights•
Agentic AI: from neurons to autonomous systems Paul Agapow Bayezian / Skipping Girl Bio
A little bit of history about how we process sequences of data
Neural Networks: the foundation • ANNs, deep learning, etc. • Way of transforming inputs to outputs • With enough hidden layers can represent any manifold • Learns “representations”, higher order features • Limited in size until training breakthroughs about 2012 • Evolutions: • Convolutional NNs: nodes are connected to their neighbourhood • Recursive NNs / LSTMs: work on sequences, a step at a time
Attention: the breakthrough mechanism • Problem: RNNs represent a sequence as a single fixed vector – slow, not parallelizable • Attention (2015) is a way of selectively looking at context, the rest of the input • Multi-head attention: multiple processing in different ways (proximity, grammar, cross-referencing) • Parallelizable across the full sequence
Transformers: the breakthrough architecture • Vaswani et al. 'Attention Is All You Need' (2017) • What if you ditched recurrence entirely and just used attention, adding position encodings so the model knows token order • Scales predictably with compute • BERT
LLMs: scale is all you need • The big split: • BERT: reads and encodes whole sequence, trying to predict parts, good at ”understanding” • GPT: reads sequences LtR, trying to predict the next token, naturally generative • LLMs: GPT + a huge number of parameters (weights), massive training data, tuned with human preferences • Result: apparently emergent capabilities, reasoning, code, arithmetic. Everyone loses their goddamn mind.
LLMs: truly weird • LLMs are like amnesiacs - they remember nothing between interactions. Each request contains your previous interactions and new information, which it all processes. A conversation is a series of independent inferences • History can fill up the context • LLM (like all models) are compressions & “hallucinations” are compression artefacts • Sycophancy • Prompt sensitivity • Ignores middle of context • Lack of actual reasoning …
From LLM to Agent • An agent is an LLM in a loop, orchestrated to do a multistep task • Observe (request, or result of last action) • Think (compute) • Act (via tools, etc.) • Repeat … • Agent can be provided with • Tools (APIs, file system, database) • Memory (context, logs, vector dbs) • Stopping condition & other orchestration • An agent is an LLM with capabilities, executing a task autonomously
What could you use an agent for? • Like a ring-fenced LLM, kept on task • Tasks too long for a single prompt, decomposing a multi-step task • Repeated tasks over live data • High-volume expert but “deterministic” work • Things with clear success criteria & thus automated evaluation • Examples: trial protocol review, literature curation, data QC, document generation, data translation …
Best practices for agentic AI • Start out simple • What needs to be an agent and what can be orchestration or simple logic • Spend a lot of time on evals – tests to see that the agents are performing tasks correctly • Each agent should have a well-defined “small” task, with only a few steps • Can use agents to check the results of other agents • What’s the cost of being wrong?
“Vibe coding” • If you can use agents to write documents, you can use them to write code • More than just auto-completing or generating and pasting code, can generate & deploy whole projects • Everyone loses their goddamn mind • From coining of term to publishing of first 2 books: 84 days • Possible maintenance nightmare • Some interesting suggestions about specification and test-driven development From https://mockflow.com/blog/what-is-vibe-coding
Advice, tools and directions • Different LLMs have different strengths • Lower paid levels should be fine • Vibe coding • Antigravity, ClaudeCode • Interesting Github developments • MCP (Model Context Protocol) as an emerging standard for tool APIs • Small models • Read Andrej Karpathy • Ignore the extremists
What about ClawdBot / MoltBot / OpenClaw? • Making an agent was a major software effort • ClawBot was a quick hack to bring together a bunch of capabilities to let people build & run agentic systems on their local machines • Reading email, managing calendars, deploying code, running terminal commands … • Everyone loses their goddamn mind • Crazy insecure, breaks all the time, unbounded behaviour, over-powered …