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 Engineering Principles

- When to Use Loops (And When Not To)

You will leave with a concrete pre-deployment checklist and the architectural frame that the model providers don't ship with the SDK. Practical, production-focused, no doom.

Signal Over Noise is a weekly intelligence briefing on Agentic AI for enterprise technology leaders and builders, hosted by Srini Karlekar.

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 Isn't Understanding

- Session Architecture: The Sandbox Is Not Optional

- A Distinct Threat Model

- The Hybrid Strategy: Computer Use as the Last Resort

You will leave with a concrete pre-deployment checklist and the architectural frame that the model providers don't ship with the SDK. Practical, production-focused, no doom.

Signal Over Noise is a weekly intelligence briefing on Agentic AI for enterprise technology leaders and builders, hosted by Srini Karlekar.

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 specific failure modes most teams aren't correcting for

• How your golden dataset is decaying right now (half-lives measured in weeks, not years) and what continuous curation looks like in practice

• The offline-to-online eval gap: what CI catches, what production catches, and what falls through the space between them

• Eval-Driven Development explained — why writing the rubric before the agent changes everything

• The five ways your eval pipeline itself becomes an attack surface, and the structural defenses that close each one

This episode is for engineering leaders and AI builders who have already shipped something and think they're covered. We skip the 101s and go straight to the gap between "we have evals" and "we have an eval discipline" — and what it costs when that gap goes unaddressed.

Practical, balanced, and built for teams who want to leave with something to act on Monday morning.

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 and the common pitfalls to explore how pioneering development teams are building robust "conversation graphs". Discover the massive competitive moat created by implementing a structured "Message Contract"—using cryptographically verified identity, strict scopes, and evidence-attached claims to establish seamless trust boundaries between cooperative agents. Whether you are an enterprise technology leader or a hands-on builder, this episode delivers an inspiring blueprint for mastering the next major era of software architecture.

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 of a runaway agent loop that burned $47,000 in 11 days. It provides engineering teams with the architectural discipline needed to stop paying for the same thought twice. Listeners will walk away with actionable, framework-level strategies—such as implementing result-compression layers, setting explicit "thinking" budgets, and building per-task evaluation matrices—to achieve full-spectrum token visibility and dramatically reduce their production AI costs.

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-Driven Development (SDD): Locking in machine-testable intent before the loop runs.

Autogenesis: Systems that safely evolve their own tools and schemas.

Read the full newsletter at: signalovernoise.karlekar.cloud/issue-011.html

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 Garry Tan's GBrain.

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.

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 nightmares of overhauling the enterprise stack.

Watch them battle through the hype and ultimately find common ground: a targeted "Knowledge Lake" that models just 5% of high-signal data to power AI reasoning, complementing—not replacing—your existing infrastructure.

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 databases become the native command center for autonomous agent swarms.