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)