What does it really take to move Agent AI from theory into production? Can we trust autonomous agents to make decisions without spiraling into hallucinations? And when speed, scale, and security collide, how do developers keep control?

In this episode, Sohaib Anwar reveals the hidden mechanics of building reliable agent workflows from serverless deployments to guardrails that keep AI honest.

Episode Highlights

• POC → prod: serverless + WebSockets
• Loop control
• Hallucination hygiene
• Security guardrails
• And more…

Timestamps

0:00 – Introduction and guest background

2:14 – Early journey into AI and first projects

6:26 – Challenges in learning machine learning and algorithms

10:01 – Discussion on transformer models vs. traditional ML

14:41 – Observability of AI Agents

18:27 – How wide or narrow to guide agent behaviour...

22:17 – The future of agent AI and closing thoughts

Tools & frameworks mentioned

• LangChain / LangGraph
• LangSmith
• Traceloop/OpenLLMetry
• Redis
• WebSockets
• Kafka
• Datadog
• AWS CloudWatch

Guest: Sohaib Anwar

Sohaib Anwaar is an AI Engineer specializing in AgentOps, multi-agent systems, and production-grade LLM integrations. He has worked on end-to-end agentic architectures, scalable orchestration layers, and real-time context-aware automation for enterprise environments. His focus is on building reliable, adaptive, and high-performance agent workflows aligned with real-world business constraints.

Reach out to him and connect on:
Instagram: https://www.instagram.com/sohaibanwaar.life?igsh=bzk1bnZqa252bXJr&utm_source=qr
LinkedIn: https://www.linkedin.com/in/sohaibanwaar/