Building an AI Agent that thinks and grows with you

Most “How to build an agent” articles dive straight into code. Yet every breakthrough agent we’ve seen begins long before the first pip install. It begins with three quiet habits:

Hold those habits in mind; the technical work below will feel far less daunting and far more rewarding.

What the article covers (and what’s missing)

The article walks through individual code blocks we used across several agent-related use cases. It includes shared snippets and search snippets (that you could share through Pieces) that highlight specific functionalities. 

On their own, they’re useful but they aren’t enough to build a fully agentic system.

For example, you’ll notice the Qdrant snippet references an ltm collection that isn’t defined or contextualized anywhere. 

Without that context, you might be left guessing. 

So while the code reflects the concepts mentioned in the article, it shouldn’t be treated as a ready-made framework.

In fact, building agents isn’t really about copy-pasting someone else’s structure. 

As our Head of ML Engineering puts it:

“People build agents in all kinds of ways — there’s no single ‘right’ framework. But there are three common components you’ll see in nearly every serious implementation:

1. Thought – The LLM decides what the next step should be.

2. Action – The agent executes an action via a tool or API.

3. Observation – The model reflects on the tool’s output and decides what to do next.”

Here is an excellent resource for people looking to dip their toes into designing agents.

Why did we share these blocks anyway?

Even if the article doesn't give you a step-by-step walkthrough of a production-grade agent, these blocks were shared to spark ideas and encourage experimentation. 

They represent the building materials, not the blueprint.

And if you combine these with the three foundational habits: capturing, asking, and reflecting, you’ll be in a much stronger position to build an agentic system that’s not just functional, but genuinely effective.

So let’s roll in…

Give your agent a memory layer 

A model without memory is a clever parrot. 

A model with memory becomes a partner. Your first architectural decision, vector store? graph? Hybrid? 

It sets the stage for every later success or failure.

Spin up a memory store

Quick semantic search

pip install qdrant-client openai

Relationship-rich graph search

  -e NEO4J_AUTH=neo4j/password \
  -p7474:7474 -p7687:7687 neo4j:latest
pip install neo4j

Hybrid

Run both; store embeddings in Qdrant and edges in Neo4j.

As you skim blog posts on pinecone vs. qdrant vs. weaviate, Pieces silently clips code snippets, diagrams, and pros/cons tables.

Next month, when you wonder “Which store was fastest with 1 M vectors?” you won’t open a browser, you’ll open Pieces.

Design a minimal schema

Tip: Store everything in UTC; convert in the UI.

Write a context retriever

def fetch_context(query: str, top_k: int = 5):
    embedding = openai.embeddings.create(
        model="text-embedding-3-small",
        input=query
    ).data[0].embedding
    matches = qdrant_client.search(
        collection_name="ltm",
        query_vector=embedding,
        limit=top_k,
        with_payload=True
    )
    return [m.payload for m in matches]

Verify with ChatGPT

I’ve stored embeddings in Qdrant as above. How can I batch-insert documents and ensure cosine similarity is configured correctly?

Keep drilling down until you can explain it back without notes.

Curiosity muscles prepare you for the bugs you haven’t met yet.

Reflection checklist

Pieces becomes your architectural conscience, surfacing the notes you wrote to your future self.

Build a reasoning engine 

Memory is useless if your agent can’t think with it. The reasoning layer turns raw context into helpful action.

Choose a model

Draft a base prompt

SYSTEM_PROMPT = """
You are a step-by-step reasoning agent. 
Use the provided context first; only fall back to general knowledge if needed.
"""
USER_TEMPLATE = """
Question: {question}

Context:
{context}

Answer in JSON with keys: "answer", "thought_process".
"""

Add a thought chain

from langchain.chains import LLMChain
prompt = PromptTemplate(
    input_variables=["question", "context"],
    template=SYSTEM_PROMPT + USER_TEMPLATE
)
chain = LLMChain(llm=model, prompt=prompt)

Validate outputs

import json, re
def safe_call(q, ctx):
    raw = chain.run(question=q, context=ctx)
    try:
        data = json.loads(raw)
        assert "answer" in data
        return data["answer"]
    except Exception:
        return "Sorry, I’m unsure. Can you clarify?"

Ask ChatGPT when stuck

• Prompt:
  Why does my LangChain chain sometimes return stray markdown around JSON?

• Follow-up:
  Show me a regex to strip triple-backtick blocks safely.

• Keep asking until the answer feels mundane; then, implement.

Reflection points

Install a learning loop

Static agents age like milk. A learning loop keeps them fresh.

Capture feedback

feedback_db.insert({
    "query": query,
    "response": answer,
    "grade": "good" if user_upvote else "bad",
    "timestamp": datetime.utcnow()
})

Decide: Fine-Tune vs. RAG Update

Automate evaluation

1. Latency: time.perf_counter() around LLM call.

2. Accuracy: Diff model answer vs. gold JSON.

3. Token cost: usage.total_tokens.

Store dashboards in Grafana. Pieces will remember which panel you tweaked when a metric spikes six months later.

Use ChatGPT as mentor

Prompt:
Suggest three automatic metrics to detect model drift in an RAG pipeline. Explain pros and cons.

Persist this conversation in Pieces; future you will re-read it during a post-mortem.

Expose a human interface 

Even the smartest agent dies in obscurity if users can’t reach it.

REST API With FastAPI

from fastapi import FastAPI
app = FastAPI()

@app.post("/ask")
async def ask(q: Query):
    ctx = fetch_context(q.text)
    ans = safe_call(q.text, ctx)
    return {"answer": ans}

Slack Slash-Command

@app.command("/askagent")
def handle(ack, body, respond):
    ack()
    q = body["text"]
    ctx = fetch_context(q)
    ans = safe_call(q, ctx)
    respond(ans)

Web chat widget

Reuse the REST endpoint. Keep payloads JSON-only.

Pieces reminder: It stores every API contract, auth header, and error pattern you define, so the V2 mobile app will inherit lessons automatically.

Monitor with empathy

AI monitoring isn’t just CPU graphs; it’s human impact metrics.

Store incident playbooks in Pieces so on-call engineers wake up to context, not chaos.

A day in the life with questions, memory, reflection 

Picture this flow:

1. Morning

   • You start coding. Autocomplete fails on an obscure npm error.

   • Ask ChatGPT: “Common causes of MODULE_NOT_FOUND / bcrypt on macOS M1?”

   • Follow-up: “What env flag fixes it?”

   • Copy the fix into VS Code. Pieces captures Q&A + solution.
     
     

2. Afternoon

   • You design a vector schema. Google a dozen tutorials.

   • Pieces clips them automatically.

   • Confused? Ask ChatGPT: “Vector vs. HNSW, why pick one over the other?”

   • Flag the best answer as insightful.
     
     

3. Evening

   • Your agent misclassifies a user query.

   • Recall: Pieces surfaces the “regex guardrail” note you wrote last month.

   • Implement fix. Commit. Push.
     
     

Memory + questions + reflection formed a virtuous cycle. No hype, just steady momentum.

Frequently asked “Stuck” moments

The partnership paradigm 

Building one agent is a sprint. Building a career of agents is a marathon of compounded insight. 

Pieces is the notebook you never lose. ChatGPT is the colleague who never tires of questions.

Together they create a loop:

1. Curiosity sparks a question.

2. ChatGPT answers; you experiment.

3. Pieces captures outcome and context.

Next project starts one step higher.

That loop outperforms any single “genius stack” because it scales you, your intuition, your taste, your memory.

Spin up the memory store. 

Install Pieces. 

The next time you’re stuck, open ChatGPT and keep asking “why?” until it’s obvious. Then let Pieces file the breakthrough where future-you will find it instantly.

That’s how you build agents and expertise that think and grow alongside you.