Signal Over Noise

Srini Karlekar

Weekly Agentic AI Briefings for enterprise professionals. Cutting through the hype to deliver what matters in AI agents, frameworks, security, and governance.

  1. Sep 8

    Why AI Value is a Management Problem, Not a Technology Problem

    A conversation between two hosts unpacking why AI value is a management problem, not a technology problem — adapted from a Signal Over Noise guest lecture on AI strategy and business transformation. The starting point: every serious AI vendor's model is good enough now. So why do most companies still struggle to show a real return? The hosts dig into three business decisions that actually separate the companies getting value from the ones burning budget — measurement, verification, and governance. In this episode: The hosts trace a 2022 "AI" drive-thru that turned out to be mostly human workers overseas — and the SEC enforcement action that followed — to open a wider discussion about how easy it is for a working, useful product to be oversold as more autonomous than it is. From there, they walk through: Measurement — why most AI pilots never show up on a P&L, and a simple formula for turning "adoption is up" into a number that survives a board review Verification — "agent-washing," and a practical way to sort real autonomous systems from relabeled automation Governance — what happens when companies adopt AI agents faster than they can track them, and two lightweight fixes any team can start without an engineering budget They close on a human wrinkle: research suggesting that the more people trust AI, the less they verify it — and what that means for anyone signing off on an AI initiative in the next few years. No technical background required to follow along — just three questions worth asking in any AI review.

  2. Sep 6

    Agent-Washing: Navigating the Illusion of AI Autonomy

    Every vendor on the market now calls their product an "AI agent." Most of them are chatbots in a trench coat — and the gap between the two just became a boardroom-level risk. In this debate-style episode, two hosts dig into agent-washing: the practice of relabeling old software as autonomous AI to chase a bigger budget line. We trace it back to the Mechanical Turk — the 18th-century "chess-playing machine" that was actually a man hidden in a cabinet — and forward to real 2025-2026 cases where the SEC and a federal prosecutor found the same trick behind a drive-thru voice assistant and a "one-click checkout" app. The hosts argue through the only test that actually matters: who decides the next step at runtime, the code or the model? That question sorts every product on an autonomy ladder from scripted bots to true open-ended agents — and reveals why most things sold as "agentic AI" today sit lower on that ladder than their pitch decks suggest. They also make the case, seriously, for why staying at a lower rung is sometimes the smarter engineering call, not a lesser one. It closes with six concrete questions to bring into your next vendor demo — the kind that separate a real agent from a well-rehearsed one. Based on Issue #23 of Signal Over Noise, Srini Karlekar's newsletter on agentic AI architecture and enterprise AI strategy. Full issue: signalovernoise.karlekar.cloud/issue-023.html

  3. Aug 29

    The Fifth Gauge: Measuring Business Value in Agentic AI

    Your board review has a slide for AI. Adoption is up. Usage is climbing. Uptime is 99.95%.Every number is green. And when someone asks "how much value did this actually generate," not one of them answers it. Most dashboards were never built to answer that question — because most teams are still measuring agentic AI like it's a normal feature. It isn't. It's probabilistic, it drifts, and "adoption" tells you nothing about whether it's actually worth what it costs. In this podcast, I lay out the fifth gauge every product leader needs, but almost nobody has: a real Net Business Value formula, measured against an actual baseline — not a vibe.

  4. Aug 8

    The Model Lease: You Don't Pick a Model. You Pick an Expiration Date.

    A friendly, rigorous debate on the question most model-selection conversations skip past: should you be running open weights at all? One host makes the case that a managed closed API is a legitimate engineering choice — you rent capability and spend scarce engineers on the product. The other argues that every model in your stack is a lease with an expiration trigger, and open weights are the one where you hold the paper. Six rounds: Open weights or not — and why enterprise adoption fell from 19% to 11% in the year the licenses got permissive Standardize on one model, or decompose the agent loop step by step Turn the reasoning-effort dial before you change the engine Self-hosting economics — the GPU bill is the small number The specialization ladder: prompting and RAG versus fine-tuning What benchmarks miss — tool-call reliability, and pass^k versus pass^1 Based on Signal Over Noise Issue #21, "You Don't Pick a Model. You Pick an Expiration Date"

  5. Aug 2

    The Comprehension Debt Economy: Why Al Makes Us Faster but Shallower

    AI is making every profession faster — and some of them shallower. In this episode, we debate "comprehension debt": the widening gap between what professionals ship and what they actually understand before shipping it. Expect pushback, disagreement, and a few uncomfortable admissions as we dig into the 2025 evidence across software, medicine, and law — developers who felt 20% faster while measurably slowing down, doctors whose skills quietly decayed once AI was removed, lawyers sanctioned for citations they never checked. Is this a real crisis or just old-fashioned tech panic in new clothes? Where's the line between smart delegation and silent skill loss? And as AI agents take on more of the work, does the gap shrink — or multiply? Two perspectives, one uncomfortable question: are we still the authors of our work, or just the ones signing off on it?

  6. Jul 27

    The Non-human Identity Explosion & What To Do About It

    For every human employee, your company now runs 80+ non-human identities — most watched by nobody. You spent years locking down human logins: MFA, SSO, quarterly reviews. Then last quarter, forty AI agents went live — code review, support, finance — each running on a static API key sitting in a config file, an env variable, or the prompt itself. No MFA, no rotation, no audit trail. The real danger: that key no longer sits in a locked server room. It lives inside a probabilistic system that can be prompt-injected into leaking back in a trace or a customer reply. Today: why the non-human identity crisis behind your credential model won't survive the next 12 months, and what the industry's converging on instead — credentials that live minutes, not months, scoped to one task, bound to one workload. Agent security won't be won on clever prompts. It'll be won on who holds the keys, and for how long.

  7. Jul 21

    The Interface Problem: When Agents Outgrow the Chat Window

    The chat window is the laziest interface an agent can produce. 70% of enterprise AI pilots are still returning markdown. The model gets smarter every quarter. The chat window doesn't. Somewhere between the tokens an agent emits and the pixels a human touches, there's an architectural layer most teams haven't named — and most stacks don't have. That layer is the render layer. And in 2026, it stopped being optional. In Issue #18 of Signal Over Noise, I map the full landscape: the four rungs of agent output, the AG-UI vs. MCP-UI standards fork, how OpenAI, Microsoft, and Anthropic are each making different bets on generative UI, the security threats nobody talks about, and a build-vs.-buy framework for picking your path. Three forces make this urgent now: EU AI Act Article 50 takes effect August 2nd, 2026. The infrastructure excuse has expired, and half the Fortune 500 already has generative UI in production.

  8. Jul 13

    Code Is the New Tool Call

    The smartest agents shipping into production this quarter no longer pick tools from a list. They write programs. In this episode: - The paradigm: a menu vs a programming language - What exactly is a code-writing agent? - How the execution loop actually runs - The five-tier sandbox model - Why it wins on multi-step tasks: the latency math - When NOT to use a code agent 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.

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About

Weekly Agentic AI Briefings for enterprise professionals. Cutting through the hype to deliver what matters in AI agents, frameworks, security, and governance.

Information

  • Creator
    Srini Karlekar
  • Years Active
    2026
  • Episodes
    24
  • Rating
    Clean
  • Copyright
    © Srini Karlekar 2026
  • Show Website
    Signal Over Noise