Your First Agent Will Fail. Build for It.

Almog Baku, a three-time founder now building AI-agent reliability infrastructure and the founder of GenAI Israel's 7,000-person community, has shipped more than 30 AI agents into production. His hard-won rule: build the agent expecting an epic failure, then design the systems that learn from it before real users hit send.

Aug 25, 2026 34:51

Almog Baku

Expert Insights

Almog Baku has shipped more than 30 AI agents into production and founded the 7,000-strong GenAI Israel community. Now he's building infrastructure to help teams do root-cause analysis on agents once they're live. His argument in this episode: most teams treat building the agent as the whole job. Realistically, it’s half of it.The other half is designing, in advance, how you'll learn why it fails.

Hear him outline:

Why he tells clients to warn their CEO in advance that the first production version will be "an epic failure," and builds the learning infrastructure around that assumption
The LLM Triangle Principles: an SOP modeled on a real domain expert, paired with the right model, prompting technique, and data- in that order, not model-first
Why the Valentine's Day game he built for his wife in four days turned out only "moderate at best," and what that taught him about skipping domain expertise
Why he treats eval scores as clues that point to a trace worth reading
The difference between agentic workflows he'll let run loose (personal tools like Claude Code) and production pipelines that need staged quality
The "credit assignment" problem: when a chain of sub-agents fails, figuring out which one is to blame, and why he thinks it's currently unsolved at the infrastructure level

We need to tell our boss, our boss's boss, the CEO, whatever. They need to understand that this is gonna be an epic failure. But because we know it's gonna be an epic failure, we are gonna place many different ideas, architectures, and infrastructure to support that and to learn from this epic failure.

— Almog Baku, Co-founder & CEO, [stealth AI-agent reliability startup]

Monterail Team Analysis

Baku's split between personal-assistant agents and production-pipeline agents is a useful lens for scoping client work early. It settles whether a project needs an autonomous agent or a structured workflow before choosing architecture.
His point that Claude Code failures usually trace back to a weak PRD, not the model, backs up what we already tell clients: time spent on the brief and spec pays off more than swapping models or tools later.
The cost detail he raises (Opus bills arriving hourly, requiring active model routing) makes a concrete case for building cost-aware model routing into agent architecture from day one.
His credit-assignment problem (working out which sub-agent in a chain caused a failure) is the same issue we hit on multi-agent client builds. Every company currently rebuilds this from scratch, and it's now the premise of his own stealth startup: an industry-wide infrastructure gap.
Treating eval scores as clues argues for writing that expectation directly into the SOW ("no single quality score blocks release"), so a client can't later hold launch hostage to a perfect eval number.
The Valentine's Day anecdote works as a quick gut check for any team building agents outside their own expertise: without an SOP modeled on someone who's done the task, the agent produces a plausible-but-wrong result with total confidence.
His "AI won't ask for your Google Analytics" point: that agentic tools won't surface a relevant data source on their own unless a workflow explicitly steers them there is worth building into any research or analysis workflow we hand off to an agent.