Foundations
Conceptual clarity earned from building at scale
Foundations
Conceptual clarity earned from building at scale
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.
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.
Tools & Techniques
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.
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.
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.
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.
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.
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.
Pattern Recognition
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.
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.
Major vendors now bundle evaluation tools with agent platforms rather than expecting customers to build testing infrastructure themselves.
AWS shipped 13 evaluation systems December 2nd. Salesforce added Testing Center in November. Evaluation became a product feature.
Non-deterministic behavior across conversation turns breaks traditional QA. Companies can build agents faster than test them reliably.
Evaluation complexity is the actual adoption barrier. Vasi Richardson called it "the biggest fear people have" about agent deployment.
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.
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.