Build and deploy AI agents in 5 lines of code.

# Step 1: Provision a vector DB + embedding pipeline
pinecone.init(api_key=...), index = pinecone.Index("my-index")
embedder = SentenceTransformer("all-MiniLM-L6-v2")
chunks = [text[i:i+512] for i in range(0, len(text), 448)]
index.upsert([(f"doc_{i}", embedder.encode(c)) for i, c in enumerate(chunks)])

# Step 2: Manual PII scrubbing (fragile regex, hope for the best)
re.sub(r"[A-Z][a-z]+ [A-Z][a-z]+", "[REDACTED]", prompt)

# Step 3: Tool dispatch + retry logic
def call_with_retry(messages, max_retries=3):
    for attempt in range(max_retries):
        try: return client.chat.completions.create(...)
        except: time.sleep(2 ** attempt)

# Step 4: Session memory via Redis
redis_client.setex(f"ctx:{session_id}", 3600, json.dumps(messages[-20:]))

# Step 5: Flask server, auth, rate limiting, CORS, logging...
# Step 6: Write your Dockerfile, k8s config, CI/CD pipeline...
# Step 7: Finally — your agent runs. Maybe.