Agentic AI in 2025: Build a “Downloadable Employee” with Large Action Models + RAG 2.0
Date: September 8, 2025
Author: LK-TECH Academy
Today’s latest AI technique isn’t just about bigger models — it’s Agentic AI. These are systems that can plan, retrieve, and act using a toolset, delivering outcomes rather than just text. In this post, you’ll learn how Large Action Models (LAMs), RAG 2.0, and modern speed techniques like speculative decoding combine to build a practical, production-ready assistant.
1. Why this matters in 2025
- Outcome-driven: Agents plan, call tools, verify, and deliver results.
- Grounded: Retrieval adds private knowledge and live data.
- Efficient: Speculative decoding + optimized attention reduce latency.
2. Reference Architecture
{
"agent": {
"plan": ["decompose_goal", "choose_tools", "route_steps"],
"tools": ["search", "retrieve", "db.query", "email.send", "code.run"],
"verify": ["fact_check", "schema_validate", "policy_scan"]
},
"rag2": {
"retrievers": ["semantic", "sparse", "structured_sql"],
"policy": "agent_decides_when_what_how_much",
"fusion": "re_rank + deduplicate + cite"
},
"speed": ["speculative_decoding", "flashattention_class_kernels"]
}3. Quick Setup (Code)
# Install dependencies
pip install langchain langgraph fastapi uvicorn faiss-cpu tiktoken httpx pydantic
from typing import List, Dict, Any
import httpx
# Example tool
async def web_search(q: str, top_k: int = 5) -> List[Dict[str, Any]]:
return [{"title": "Result A", "url": "https://...", "snippet": "..."}]
4. Agent Loop with Tool Use
SYSTEM_PROMPT = """
You are an outcome-driven agent.
Use tools only when they reduce time-to-result.
Always provide citations and a summary.
"""5. Smarter Retrieval (RAG 2.0)
async def agent_rag_answer(q: str) -> Dict[str, Any]:
docs = await retriever.retrieve(q)
answer = " • ".join(d.get("snippet", "") for d in docs[:3]) or "No data"
citations = [d.get("url", "#") for d in docs[:3]]
return {"answer": answer, "citations": citations}
6. Make it Fast
Speculative decoding uses a smaller model to propose tokens and a bigger one to confirm them, cutting latency by 2–4×. FlashAttention-3 further boosts GPU efficiency.
7. Safety & Evaluation
- Allow-listed domains and APIs
- Redact PII before tool use
- Human-in-the-loop for sensitive actions
8. FAQ
Q: What’s the difference between LLMs and LAMs?
A: LLMs generate text, while LAMs take actions via tools under agent policies.
9. References
- FlashAttention-3 benchmarks
- Surveys on speculative decoding
- Articles on Large Action Models and Agentic AI
- Research on Retrieval-Augmented Generation (RAG 2.0)
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