Signal Over Noise

Retry logic solves the tool call. Loop engineering solves everything above it. Most teams have the first. Almost no one has the second -- and that gap is exactly what production exposes. In this episode: - The Gap Retry Logic Doesn't Close - Six Loop Archetypes -- Know Which One You're Running - Why Loops Are the Billing Multiplier Nobody Puts in the Estimate - The Five Loop Failure Modes - Six En...

Computer use agents can access every system a human can reach -- including the majority of enterprise workflows that have never had an API and never will. What teams reaching for this capability are discovering, painfully, is that the capability is not the hard part. In this episode: - The Glass Wall: What Computer Use Actually Is - Two Ways to See the Screen - The Grounding Problem: When Seeing I...

Your eval dashboard is green. Your agent is wrong. And nothing in your stack can tell the difference. In this episode of Signal Over Noise, we unpack one of the most dangerous blind spots in enterprise AI in 2026: the gap between having an eval pipeline and having an eval discipline. What you'll get from this episode: • Why a confidently wrong LLM judge is worse than no judge — and the six specifi...

What happens when you need more than one AI agent to get the job done? In this episode, we break down the natural engineering evolution from single-agent setups to highly scalable multi-agent systems. Moving past single agents isn't just about scaling up—it’s a exciting categorical shift that mirrors the classic software evolution from monoliths to microservices. We step away from the tech-hype an...

This episode exposes the "Phantom Meters" of Agentic AI—the hidden, compounding token costs that standard observability dashboards fail to track. It breaks down critical architectural blind spots, including the reasoning tax of chain-of-thought models, massive token waste from raw tool result injections, context poisoning, and multimodal billing surprises. It also unpacks a real-world case study o...

Prompt patching is a dead end for enterprise AI. In this episode of Signal Over Noise, we dismantle the culture of "vibe coding" and explore the rigorous harness engineering required to build production-grade agentic systems. We break down the three-layer architectural arc for compounding intelligence: Reflexion: Teaching agents to learn from failure without altering model weights. Specification-D...

Standard RAG pipelines are bleeding compute. Every time a chat session closes, your organization pays a hidden "retrieve-every-time" tax to reconstruct that intelligence from scratch. In this podcast, we break down the architectural fix: Knowledge Compilation. Learn how to pay the synthesis token tax once at ingest, and query it for nearly free forever using frameworks like Karpathy's LLM Wiki and...

In this fiery debate, our hosts clash over the future of enterprise data in the age of autonomous AI, based on Srini Karlekar's article, "Your Data Stack Wasn't Built for This." An AI visionary argues that slow, traditional data warehouses will cause catastrophic AI failures, demanding a shift to Agentic DataOps. A pragmatic data architect pushes back hard on the impossible costs and engineering n...

Why do AI agents that crush the demo disintegrate the moment they hit production? In this episode, we break down the "Vibe Coding" hangover and the transition to Harness Engineering. We explore why the model is just a commodity you rent, while the infrastructure—the harness—is the moat you build. Learn how to stop coding on vibes and start building agentic systems that actually scale.

How the POLE+O+D model — evolved from Scotland Yard intelligence doctrine — transforms enterprise data from a passive archive into active intelligence. Covers the origins of POLE+O in UK policing, the fatal blind spot of capturing what happened but not why, the Decision node extension that enables auditing compliance and AI reasoning, why RDBMS fails structurally for agentic AI, and how graph data...

CI/CD pipelines, unit tests, and blue-green deployments were built for deterministic software. Agentic AI breaks every assumption. This episode covers why traditional DevOps fails for autonomous agents, the four pillars of Agentic DevOps (Evaluate, Observe, Govern, Adapt), shadow mode deployment, canary agents, LangSmith and Ragas in production, and the governance layer that keeps agents from goin...

Enterprise testing frameworks were not built for agents that plan, remember, and make decisions across multi-step pipelines. This episode covers why unit tests fail for agentic systems, how to design evaluation frameworks for non-deterministic behavior, what tools like Ragas, LangSmith, and PromptFoo actually test, and the architectural patterns — sandboxing, canary agents, shadow mode — that make...

Agentic AI security is not application security with extra steps — it is a fundamentally different problem. This episode dismantles the perimeter myth, walks through the Lethal Trifecta of indirect prompt injection, memory poisoning, MCP supply chain attacks, and east-west LangGraph pipeline cascades, then closes with concrete architectural defenses: per-action authorization, zero-trust agent boun...

Srini Karlekar explores the "Always On" Memory Agent pattern — LLM-driven persistent memory without vector databases. Why it's brilliant for some use cases and a compliance disaster waiting to happen in others. Covers architecture tradeoffs, audit trails, GDPR implications, and when NOT to use it.

The AI industry is moving past simple chatbots to agentic AI - digital workers that can complete multi-step tasks on their own. This week: OmniGAIA, CrewAI v1.0, LangGraph, critical security vulnerabilities, and why human-in-the-loop is no longer enough.

Srini Karlekar explores the shift from Generative AI to Agentic AI through the lens of the travel industry. Covering the Agent Maturity Model (L0 to L5), the Agent Squad roles, memory architecture, and 14 essential agentic design patterns.