Agentic AI: Platforms, LLMs & Specialization
A practical map of agent architectures, the platforms used to build them, open vs. closed LLMs, and how to specialize a model for a domain.
Agenda
What agentic AI is: components & types of agents
Agent platforms: n8n, ElevenLabs, LangChain & many more
Open-weighted vs. closed LLMs
Running open models: Ollama, vLLM, llama.cpp
Specializing an LLM: RAG, LoRA, QLoRA, full fine-tuning
Case studies: real agents, real tuning recipes
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SECTION 01 · FUNDAMENTALS
What is Agentic AI?
An LLM that doesn't just answer. It perceives a goal, plans steps, acts using tools, and reflects on the result, looping until the goal is done.
The LLM itself only runs at Plan and Reflect. Perceive and Act are plain code. The loop keeps turning until Reflect decides the goal is met, or a stopping rule is hit.
The building blocks of an agent
LLM core
The reasoning engine that interprets goals and decides next actions
Memory
Short-term context plus long-term stores (vector DB, files, history)
Planning & orchestration
Breaks a goal into steps; routes work across tools or sub-agents
Tools & actions
APIs, code execution, databases, search: how the agent affects the world
Perception / input
Text, voice, vision, sensor or document inputs feeding the loop
Guardrails & evaluation
Validation, human-in-the-loop checks, and monitoring of outputs
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Types of agents you'll meet
Type
How it behaves
Example
Reactive / rule-based
Fixed if-this-then-that logic, no reasoning
Simple chatbot menu
Tool-using agent
LLM calls functions/APIs to complete one task
Code interpreter, web search agent
RAG-augmented agent
Retrieves external knowledge before answering
Document Q&A assistant
Multi-agent system
Several specialized agents collaborate on one goal
Researcher + writer + reviewer crew
Autonomous workflow agent
Runs multi-step processes with minimal supervision
Automated report or ops pipeline
Voice / conversational agent
Real-time speech in, reasoning, speech out
Phone support or voice assistant
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SECTION 02 · PLATFORMS
Many platforms, because there are many purposes
Some platforms design and run the whole agent lifecycle; others let you assemble pre-built agents; others are code frameworks for full control.
Full-cycle builders
Design, connect, test and deploy an entire agent visually, no code needed
Vertical / pre-built agents
Take an existing, specialised agent (e.g. voice) and plug it into your product
Code-first frameworks
Native libraries giving full control over orchestration and logic
More guided, faster to ship
More control, more effort
A fourth layer sits underneath, and increasingly across, all three: the cloud and model-provider platforms (AWS, Google, Azure, Cloudflare, OpenAI, Anthropic) covered next.
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Full-cycle agent builders
Best for: fast delivery, non-developers, and automating existing business processes with AI steps.
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Vertical & pre-built agent platforms
You do not build the agent here. You call it: an API or SDK request from your own app, or an embeddable widget dropped onto your site, hands the task to the provider's hosted agent and gets a result back.
Biomni
Stanford's open biomedical AI agent for genomics, drug discovery, and lab tasks
Best for: one specific domain where a ready-made agent beats a blank canvas.
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Code-first orchestration frameworks
LangChain / LangGraph
Graph-based orchestration of chains, tools and state; the de-facto standard
AutoGen
Microsoft's framework for conversable, negotiating multi-agent systems
Semantic Kernel
Microsoft's enterprise SDK with plugins, planners, .NET/Python/Java support
Best for: engineering teams who need full control, custom logic, and tight integration with existing codebases.
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SECTION 02 · PLATFORMS
Cloud & model-provider platforms
The hyperscalers and model labs sit underneath, and increasingly across, the three categories already covered: they supply the models, often the infrastructure, and now their own agent-building tools too.
AWS Bedrock Agents
Framework + full-cycle
Managed agent orchestration on Bedrock foundation models, with action groups, knowledge bases, and guardrails
Google Vertex AI Agent Builder
Framework + full-cycle
Agent Development Kit (ADK) for code-first agents, plus managed grounding in Search and enterprise data
Microsoft Azure AI Foundry
Full-cycle + framework
Unified hub for models, agents, and evaluation, paired with the low-code Copilot Studio builder
Cloudflare Agents SDK
Code-first framework
Deploy agents as edge workers with built-in state via Durable Objects and very low latency
OpenAI platform
Framework + vertical
Model provider with its own agent tooling: the Responses/Agents SDK plus the GPT Store for distribution
Anthropic
Framework
Model provider (Claude) with the Claude Agent SDK and the Model Context Protocol for connecting tools
Best for: teams already standardised on one cloud or model ecosystem, who want agent tooling bundled with the infrastructure and model access they are already paying for. Many of these blend full-cycle, vertical, and framework traits at once, rather than fitting neatly into one category.
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Choosing the right platform
Full-cycle builder
Vertical / pre-built
Code framework
Cloud / model platform
Speed to build
Fast
Fastest
Slowest
Varies by tool
Control / customisation
Medium
Low
Full
High, within one vendor
Needs coding?
No / low-code
No
Yes
Depends on the tool
Best for
Automating internal processes end-to-end
A single channel, e.g. voice or phone
Bespoke products, complex multi-agent logic
Teams already standardised on one provider
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Four platforms, four concrete jobs
n8n · full-cycle builder
Literature triage
Watches a shared inbox for new preprints, tags each by topic with an LLM node, posts a ranked Slack digest every morning, built in an afternoon with no code
ElevenLabs · vertical agent
Trial-info line
A voice agent answers common questions about an open clinical trial around the clock, and hands off to a human coordinator for anything medical
LangGraph · code framework
Risk-summary agent
Given a drug name, searches ClinicalTrials.gov, cross-references adverse-event reports, and drafts a one-page risk summary with citations
AWS Bedrock Agents · cloud platform
Submission drafting
Pulls source data from a clinical data lake through a Bedrock knowledge base, then drafts regulatory-submission sections with the platform's own audit trail and guardrails
Same underlying idea (perceive, plan, act, reflect) wired up four different ways because the job, the team, and the budget were different.
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SECTION 03 · THE MODELS
Open-weighted vs. closed LLMs
Closed (API-only)
GPT-5 · Claude · Gemini
Weights never released. Access via API. Best quality & convenience, no infrastructure, but less control, ongoing cost, data leaves your premises.
Open-weighted
Llama · Mistral · Qwen · DeepSeek · Gemma
Weights downloadable. Run & fine-tune anywhere, full data control, one-off compute cost, but you own the serving & ops.
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Running open-weighted models
llama.cpp
Lightweight C/C++ runtime for CPU and edge devices, quantized GGUF models
TGI
Hugging Face's Text Generation Inference, an enterprise-grade serving server
Rule of thumb: Ollama to prototype, vLLM/TGI to serve at scale, llama.cpp when there's no GPU.
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What fits on what hardware
Model size, not the serving tool, decides the resource question. Figures below assume 4-bit quantization; full-precision (fp16) roughly doubles to quadruples the memory needed.
Size tier
Example models
Memory (4-bit)
Typical hardware
Tool to reach for
Tiny, 1–3B
Llama 3.2 1B/3B, Qwen2.5 1.5B, Gemma 2 2B
2–4GB
Any laptop, even CPU-only
Ollama, llama.cpp
Small, 7–9B
Llama 3.1 8B, Mistral 7B, Qwen2.5 7B, Gemma 2 9B
6–10GB
One consumer GPU, e.g. RTX 3060 12GB
Ollama, vLLM
Medium, 22–34B
Mistral Small, Qwen2.5 32B, Codestral 22B
16–24GB
High-end consumer GPU, e.g. RTX 4090 24GB
vLLM, TGI
Large, 70–72B
Llama 3.3 70B, Qwen2.5 72B
40–48GB
Two 24GB GPUs, or one 48GB+ card
vLLM, TGI
Frontier, 400B+
Llama 3.1 405B, DeepSeek-V3 (671B, MoE)
200GB+
Multi-node GPU cluster, realistically cloud
vLLM, TGI, managed cloud
Rule of thumb: roughly 1GB of VRAM per billion parameters at 4-bit quantization, plus headroom for context length and batching.
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Serving methods compared
Tool
Best for
Throughput
Hardware
Ease of use
Ollama
Local dev & prototyping
Low–medium
Consumer CPU/GPU
Very easy
vLLM
Production, batched requests
High
Datacenter GPU
Moderate
llama.cpp
Edge, CPU-only, quantized models
Low
CPU / edge devices
Easy
TGI
Enterprise serving, autoscaling
High
GPU clusters
Moderate
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SECTION 04 · SPECIALIZATION
How to specialize an LLM
Increasing cost and control, left to right.
Prompt engineering
No training, instructions & examples only
RAG
Weights untouched, knowledge injected at query time
PEFT (LoRA / QLoRA)
Small adapter layers trained, base model frozen
Full fine-tuning
Every weight updated: maximum control, maximum cost
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LoRA, QLoRA & full fine-tuning
Method
What changes
GPU memory
Typical use
LoRA
Small low-rank adapters added; base weights frozen
Moderate: fits one high-end consumer GPU
Domain or style adaptation
QLoRA
LoRA adapters on a 4-bit quantized frozen base
Low: fits a single 24–48GB GPU
Resource-constrained fine-tuning
Full fine-tuning
Every parameter updated
Very high: multi-GPU / multi-node
Large-scale, high-stakes specialization
QLoRA is usually the sweet spot: near full fine-tuning quality at a fraction of the memory and cost. The case study next shows one running on real hardware.
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SECTION 05 · CASE STUDY
A bio-computational research agent
Goal: given a novel protein sequence, return literature-backed hypotheses about its function and off-target risks.
Platform: LangGraph
Graph of nodes: BLAST search → UniProt lookup → PubMed retrieval → synthesis
Model: BioMistral-7B
QLoRA fine-tuned on ~60k PubMed/PMC abstracts + UniProt annotations, 4-bit
Output
Cited function hypothesis, homologous domains, and an off-target binding flag for the wet-lab team to verify
Why QLoRA here: ~14GB VRAM, <8 hours on a single A100, no multi-GPU cluster needed, yet domain-accurate terminology and reasoning.
Other specialized bio-LLMs worth knowing: BioGPT (Microsoft, biomedical text) and Evo 2 (Arc Institute, a genomic foundation model trained directly on DNA sequence, not text).
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SECTION 05 · CASE STUDY
A CRISPR guide-RNA design agent
Goal: given a target gene, return ranked, ready-to-order guide RNAs, a concrete lab deliverable, not just a read.
CRISPR: a gene-editing system. An enzyme (Cas9) cuts DNA at a spot chosen by a short "guide RNA"
PAM site: a short DNA pattern the enzyme needs sitting next to its target before it can cut
On- vs off-target: does the guide cut the gene you want, or somewhere else by mistake
The agent is a thin coordinator: it calls each tool in turn and only the final ranking step runs through the LLM.
Why this is more practical: most of the work is deterministic tool calls, not LLM-generated biology, which makes it cheaper to build and easier to trust. Same pattern fits primer design, codon optimization, or epitope checks.
Key takeaways
Agentic AI is defined by the perceive → plan → act → reflect loop, not just a bigger chatbot
Platform choice is about purpose: no-code builders, vertical agents, or code frameworks each fit a different job
Cloud and model-provider platforms (AWS, Google, Azure, Cloudflare, OpenAI, Anthropic) blend all three, and suit teams already in one ecosystem
Open-weighted LLMs trade convenience for control, privacy and one-off compute cost
Ollama, vLLM and llama.cpp solve different serving problems: dev, production, and edge, and model size dictates the hardware
Specialization is a spectrum (RAG, LoRA, QLoRA and full fine-tuning); pick by budget and need
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KEEP LEARNING
Resources & courses
Hugging Face Agents Course
Free, hands-on course on building agents with real frameworks and tool use
LangChain Academy
Short, structured lessons on LangGraph: state, memory, multi-agent graphs
DeepLearning.AI short courses
Focused, 1–2hr courses: LangChain, CrewAI, RAG, fine-tuning, evaluation
Hugging Face NLP Course
Free foundations course on transformers, tokenizers and fine-tuning
Generative AI with LLMs
Coursera course by DeepLearning.AI & AWS covering the LLM lifecycle, tuning, and deployment
fast.ai: Practical Deep Learning
Code-first, project-based deep learning for people who want to build, not just read
Also worth following: r/LocalLLaMA and the Hugging Face forums for what's new; the LangGraph, CrewAI and vLLM docs for day-to-day reference.
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Thank you
Questions & discussion
Mohamad Reza Shahabian Alashti