Contextually Aware

The production curve for generating code is steeper than understanding it. Teams that win store decisions, not commits—and specs are the moat. This episode is in DRAFT and has not been published yet.

Why stuffing context into markdown files breaks production AI agents, and the six-layer memory architecture that actually works at scale. This episode is in DRAFT and has not been published yet.

Production agents hit a wall around day 30: context drift, not retrieval, is the killer. Vector stores can't manage state across multi-agent workflows. The fix is treating memory like an operating system. This episode is in DRAFT and has not been published yet.

Luis explores five conditions that should trigger a pause before deploying an AI agent—and why a queue, form, or policy often solves the problem faster than autonomy. This episode is in DRAFT and has not been published yet.

Luis walks through five coordination failures that emerge when agent systems scale past single digits—memory contention, context bloat, skill drift, context corruption, and operator blindness. The fix: a memory OS layer that governs writes, gates context, standardizes skills, checkpoints state, and This episode is in DRAFT and has not been published yet.

OpenAI and Anthropic's IPO valuations hinge on owning the work layer above AI models—not just cheap tokens, but proprietary harnesses. We explore why context, memory, and workflow engineering are where real competitive advantage lives. This episode is in DRAFT and has not been published yet.

Palantir's CEO ranted about AI pricing, a ransomware agent proved harness engineering matters more than the model, and Mem0 benchmarked the memory layer nobody is paying attention to. The real fight isn't about weights—it's about what sits underneath. This episode is in DRAFT and has not been published yet.

The job description we wrote for PMs in 2019 doesn't match the work in 2026. Luis breaks down what's actually required to survive the AI era. This episode is in DRAFT and has not been published yet.

Why the audit trail—not the agent's output—is what actually matters in production. Design it as a user interface, and autonomy becomes defensible. This episode is in DRAFT and has not been published yet.

Luis argues that static AI strategy decks are obsolete. The future is live orchestration graphs where agents execute workflows in real time while humans design the feedback loop. This episode is in DRAFT and has not been published yet.

Five product layers are crystallizing in open-source AI agent repos right now. Learn which ones matter and how to borrow the patterns before they harden into expensive platform defaults. This episode is in DRAFT and has not been published yet.

Every AI agent in production hits the same wall: they start from zero every Monday with no memory of past decisions. Luis explores why memory and governance is the one unsolved layer of the agentic stack—and what governed memory actually requires. This episode is in DRAFT and has not been published yet.

Prompt engineering is dead. The winning move is prompt systems—evolutionary optimization, self-refinement loops, harness engineering, and spec-driven contracts. Here's how to build them. This episode is in DRAFT and has not been published yet.

When you're running multiple agents in production, you need more than a dashboard—you need a real operator console. Luis breaks down what that looks like and why it matters. This episode is in DRAFT and has not been published yet.

Luis argues the comfortable narrative about PMs evolving into agent orchestrators is false. The real trend: agentic AI will automate the PM role itself, leaving only domain experts and compliance checkpoints. This episode is in DRAFT and has not been published yet.

A new paper proves what product builders have suspected: your system prompt is the least valuable part of your AI harness. The real wins come from tool architecture, middleware, and memory structure. This episode is in DRAFT and has not been published yet.

Google didn't release a smarter model at I/O 2026—it turned 12+ products into agent runtimes. The real story is distribution dominance, not AI capability. This episode is in DRAFT and has not been published yet.

The cheapest agent improvement happens before you build it. Write your eval first—it's the product spec that prevents plausible-but-untrustworthy outputs. This episode is in DRAFT and has not been published yet.

Your board is split on AI strategy. Luis breaks down the five investment motions—build, buy, borrow, hire, wait—and gives you the framework to pick the right one for each workflow. This episode is in DRAFT and has not been published yet.

Three startups shipped on schedule and hit milestones—then quietly vanished. The real moat isn't code; it's the relationship you build with customers. Read the full article: https://growthalchemylab.com/blog/software-is-dead-long-live-services This episode is in DRAFT and has not been published yet.

Most agent failures aren't bugs—they're silent context drift happening after step five. Luis Calderon reveals why vector memory fails, and what actually fixes it. Read the full article: https://growthalchemylab.com/blog/the-memory-failure-i-keep-seeing-in-agent-stacks This episode is in DRAFT and has not been published yet.

Luis Calderon maps the four failure modes that kill multi-agent systems before launch—and the fifth that breaks them in production. Most teams only fix the first three. Read the full article: https://growthalchemylab.com/blog/the-four-places-multi-agent-systems-break This episode is in DRAFT and has not been published yet.

When a single blocked write action prevented disaster, one team learned that strategic approval gates—not blanket permissions—unlock true agent autonomy while keeping humans in control. Read the full article: https://growthalchemylab.com/blog/we-blocked-one-write-action This episode is in DRAFT and has not been published yet.

Accenture built an AI agent in two weeks but took twelve months to ship it. The bottleneck wasn't technology—it was organizational. We explore the five tensions that predict whether enterprise agentic projects survive. Read the full article: https://growthalchemylab.com/blog/enterprise-agentic-projects-doomed This episode is in DRAFT and has not been published yet.

Your Code Is Trash Anyway—And That's the Whole Point Short, sharp take on why specs beat prompts, models rewrite everything anyway, and product teams that store decisions—not code—are the ones who win. Your code is going to be rewritten anyway. Models regenerate it. The next engineer rewrites it. Six months from now, nobody remembers why any decision was made. So why are you treating the code as t...