CURRENT | Foundations

Field Guide

Reading What Your System Already Knows About Inflection Points

By Rina Takahashi— December 17, 2025

Feature image for article: Reading What Your System Already Knows About Inflection Points

Your infrastructure knows it's outgrown itself before you do. The signals are there—not in dashboards or performance metrics, but in patterns most teams mistake for problems to solve. Engineers building the same abstraction for the third time. Error messages that stopped describing failures and started describing struggle. Costs that scale with complexity instead of volume. Each looks fixable. Together, they mark something else entirely. Most teams spend months fighting symptoms before they recognize what their system has been trying to tell them all along.

Field Guide

Reading What Your System Already Knows About Inflection Points

By Rina Takahashi— December 17, 2025

Your infrastructure knows it's outgrown itself before you do. The signals are there—not in dashboards or performance metrics, but in patterns most teams mistake for problems to solve. Engineers building the same abstraction for the third time. Error messages that stopped describing failures and started describing struggle. Costs that scale with complexity instead of volume. Each looks fixable. Together, they mark something else entirely. Most teams spend months fighting symptoms before they recognize what their system has been trying to tell them all along.

Tools & Techniques

Tools in Context

When Agents Need Permission

The first time your team deploys an agent that does something consequential—updating prices, flagging fraud, triggering workflows—someone asks: "But what if it's wrong?" That question lands differently when these aren't read-only operations. So teams reach for approval tools. The agent proposes an action. A human reviews it. Then, and only then, does anything happen. This isn't distrust. It's how organizations learn what their agents can actually handle.

Tools in Context

When Agents Ask for Advice Instead of Permission

You're watching another Slack notification. The agent wants approval for a pricing update based on competitor movement. You glance at the proposal, already knowing you'll approve it—you've approved fifty identical decisions this week. The bottleneck isn't the technology anymore. It's you. This is when teams shift to advisory tools: agents operate autonomously but flag strategic decisions that need human wisdom. The work changes from gatekeeping to guidance.

Tools & Techniques

Tools in Context

When Agents Need Permission

The first time your team deploys an agent that does something consequential—updating prices, flagging fraud, triggering workflows—someone asks: "But what if it's wrong?" That question lands differently when these aren't read-only operations. So teams reach for approval tools. The agent proposes an action. A human reviews it. Then, and only then, does anything happen. This isn't distrust. It's how organizations learn what their agents can actually handle.

Tools in Context

When Agents Ask for Advice Instead of Permission

You're watching another Slack notification. The agent wants approval for a pricing update based on competitor movement. You glance at the proposal, already knowing you'll approve it—you've approved fifty identical decisions this week. The bottleneck isn't the technology anymore. It's you. This is when teams shift to advisory tools: agents operate autonomously but flag strategic decisions that need human wisdom. The work changes from gatekeeping to guidance.

Tools & Techniques

Tools in Context

When Agents Need Permission

The first time your team deploys an agent that does something consequential—updating prices, flagging fraud, triggering workflows—someone asks: "But what if it's wrong?" That question lands differently when these aren't read-only operations. So teams reach for approval tools. The agent proposes an action. A human reviews it. Then, and only then, does anything happen. This isn't distrust. It's how organizations learn what their agents can actually handle.

Tools in Context

When Agents Ask for Advice Instead of Permission

You're watching another Slack notification. The agent wants approval for a pricing update based on competitor movement. You glance at the proposal, already knowing you'll approve it—you've approved fifty identical decisions this week. The bottleneck isn't the technology anymore. It's you. This is when teams shift to advisory tools: agents operate autonomously but flag strategic decisions that need human wisdom. The work changes from gatekeeping to guidance.

In Dialogue With Complexity

An Interview with the Format That Became a Protocol

In Dialogue With Complexity

An Interview with the Format That Became a Protocol

Pattern Recognition

Vendors Ship Evaluation Tools Alongside Their Agents

Something odd happened in December. AWS announced 13 pre-built evaluation systems for agents. Salesforce launched a Testing Center. Multiple vendors released evaluation frameworks within weeks of their agent products.

They're shipping the testing infrastructure because nobody knows how to test these things. Traditional QA breaks when systems are non-deterministic, operate across conversation turns, and call external tools. You can't write unit tests for hallucinations.

Companies with structured evaluation frameworks see 60% fewer production incidents. The bottleneck isn't building agents anymore. It's figuring out whether they actually work.

Pattern Recognition

Vendors Ship Evaluation Tools Alongside Their Agents

Something odd happened in December. AWS announced 13 pre-built evaluation systems for agents. Salesforce launched a Testing Center. Multiple vendors released evaluation frameworks within weeks of their agent products.

They're shipping the testing infrastructure because nobody knows how to test these things. Traditional QA breaks when systems are non-deterministic, operate across conversation turns, and call external tools. You can't write unit tests for hallucinations.

Companies with structured evaluation frameworks see 60% fewer production incidents. The bottleneck isn't building agents anymore. It's figuring out whether they actually work.

IN SUMMARY

Pattern observed:

Major vendors now bundle evaluation tools with agent platforms rather than expecting customers to build testing infrastructure themselves.

Concrete evidence:

AWS shipped 13 evaluation systems December 2nd. Salesforce added Testing Center in November. Evaluation became a product feature.

Underlying issue:

Non-deterministic behavior across conversation turns breaks traditional QA. Companies can build agents faster than test them reliably.

Key insight:

Evaluation complexity is the actual adoption barrier. Vasi Richardson called it "the biggest fear people have" about agent deployment.

Practical application:

Don't build agents without evaluation infrastructure first. Test frameworks should precede production deployment, not follow it later.

Questions Worth Asking

Most evaluation questions focus on capabilities. What can the AI do in theory? But that's not what predicts success at scale.

What matters is operational reality. How systems behave under production conditions. What they demand from your organization. Where they break and how you recover.

These questions cut through demo magic to what actually determines whether AI delivers value or becomes another abandoned pilot. We ask them not because we're skeptical, but because we've seen what happens when you wait too long to ask them.

Questions Worth Asking

Most evaluation questions focus on capabilities. What can the AI do in theory? But that's not what predicts success at scale.

What matters is operational reality. How systems behave under production conditions. What they demand from your organization. Where they break and how you recover.

These questions cut through demo magic to what actually determines whether AI delivers value or becomes another abandoned pilot. We ask them not because we're skeptical, but because we've seen what happens when you wait too long to ask them.

Integration Reality

What Breaks Between Demo and Production?

Demos run in controlled environments. Production means authentication layers, compliance workflows, legacy systems that don't speak to each other. The gap between these worlds kills more pilots than bad models. Can they walk you through the actual integration path?

Operational Burden

Does This Fade or Demand Attention?

Good infrastructure disappears. You deploy it, it runs, you move on. Bad AI demands constant tuning, manual corrections, checking outputs. If your team spends more time managing the system than using it, you're not creating value. How much attention do production deployments actually need?

Evaluation Honesty

Can You Catch Failures Before They're Expensive?

Most teams gather evaluation metrics without translating them into improvements. The metrics graveyard problem. What you need: a framework that predicts production performance, not one that generates reassuring dashboards. Does their evaluation actually prevent costly failures or just document them?

Ground Truth

What's Right When Decisions Are Subjective?

Traditional ML compares outputs against correct answers. But subjective decisions have no single right answer. That's exactly where AI promises value and where defining correctness gets messy. How does the system measure quality when ground truth is fuzzy or contested?

Human Handoff

Where Does Automation Actually Help?

Full automation sounds appealing. Augmenting humans works better. The hard part is designing handoffs where agents handle routine work but escalate judgment calls without creating friction. Does the hybrid workflow save effort or just redistribute it?

Ownership Clarity

Who Owns Outcomes When Things Break?

Most failed AI projects have vague ownership. IT builds, data science models, business waits. Nobody manages rollout, maintains the system, or tracks ROI. Without a process owner accountable for outcomes, you get abandoned pilots. Who's actually responsible here?