Optimizing Agentic AI Systems

• Perceives its environment (via inputs or sensors),
• Decides via internal reasoning or policy,
• Acts through defined tools or API calls,
• Learns from interaction history.

        Optimization                            Target Description
 | Latency Optimization      | Reduce response times through batching, caching, or model distillation
 | Memory Augmentation       | External vector or symbolic memory to enable long-term reasoning
 | Task Decomposition        | Divide-and-conquer planning for complex multi-step problems
 | Autonomy Feedback Loops   | Let the agent self-correct, retry, or ask for clarification
 | Cost-Aware Execution      | Use cheaper models/tools when high-capacity ones are unnecessary

[User] --> [Orchestrator] --> [Planner] --> [Retriever] --> [LLM] --> [Executor]
                                              ↓
                                           [Memory DB]

pip install openai langchain sentence-transformers faiss-cpu

from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, Tool
from langchain.tools import PythonREPLTool
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain.memory import ConversationBufferMemory
from langchain.prompts import PromptTemplate
import os

# === Embedding and Memory Setup ===
embedding = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
retriever = FAISS.load_local("agent_vector_store", embedding).as_retriever()

memory = ConversationBufferMemory(memory_key="chat_history")

# === Base LLM and Tools ===
llm = ChatOpenAI(model_name="gpt-4", temperature=0.3)

retrieval_tool = Tool(
    name="RAGRetriever",
    func=RetrievalQA.from_chain_type(llm, retriever=retriever),
    description="Useful for factual, context-grounded queries"
)

python_tool = PythonREPLTool()

# === Agent Initialization ===
agent = initialize_agent(
    tools=[retrieval_tool, python_tool],
    llm=llm,
    agent="chat-conversational-react-description",
    verbose=True,
    memory=memory
)

# === Agent Call ===
response = agent.run("Given my past queries, find the trend and compute average time I spent on each topic.")
print(response)

graph TD
A[User Input] --> B[Task Parser]
B --> C[Planner]
C --> D[Tool Selector]
D --> E[Code Generator]
E --> F[Test Executor]
F --> G[Evaluator]
G --> H[Memory + Feedback Update]

task = "Add login with JWT and rate limiting in ExpressJS backend"
plan_prompt = f"""
You are a senior AI software architect. Break down the following into subtasks:
'{task}'
Respond in a numbered list.
"""
plan = llm.predict(plan_prompt)
print(plan)

from subprocess import run

def generate_code(task_desc):
    prompt = f"You are an expert ExpressJS developer. Code for: {task_desc}"
    return llm.predict(prompt)

code = generate_code("Add rate limiting middleware using express-rate-limit")
print(code)

# Optionally save and run/test the code
with open("middleware.js", "w") as f:
    f.write(code)

reflection_prompt = f"""
Evaluate the generated code. Does it follow best practices? Is anything missing?
"""
reflection = llm.predict(reflection_prompt + code)
print(reflection)

      Layer                                  Technique
 | LLM Layer        | Use model routing: phi-2 for logic, gpt-4 for language
 | Retrieval Layer  | Apply semantic compression, ColBERT indexing
 | Memory Layer     | Use hybrid (symbolic + vector), SQLite-backed memory
 | Planner Layer    | Use LLMs with Chain-of-Thought prompting or symbolic planners (e.g., pyDAG)
 | Executor Layer   | Implement Docker-based sandboxing with CPU/GPU fallback

• Task success rate (e.g., tests passed / tasks attempted)
• Latency (end-to-end inference time)
• Token usage / Cost
• Memory recall accuracy
• Autonomy level (e.g., how often it asks for help vs retries)

• Multi-modal agents (vision + language + action)
• Self-evolving agents (neural architecture search + self-correction)
• Collaborative agent swarms (multi-agent simulations)
• Federated and local agents (privacy-aware edge deployments)