Every engineering org has pointed an AI agent at production by now. Few have engineered the context and economics that decide whether that agent survives real volume. This whitepaper breaks down why the operations gap became an economics problem.

In this whitepaper, you'll learn:

• Why token consumption, not model choice, decides if AI ops pays for itself past the pilot
• Why Mean Time to Understand, not resolution speed, is the real bottleneck in production incidents
• How context engineering cuts cost and boosts accuracy at once, replacing the tradeoff most teams assume exists
• A buyer's framework for evaluating a production ops agent built to last past the demo

The practical takeaway for a CTO is a shift in the question. The question is not "which model is smartest," or even "which vendor has an agent." It is "who has engineered the context and economics so that autonomy is reliable, affordable, and defensible at the scale I actually run".

Every engineering org has pointed an AI agent at production by now. Few have engineered the context and economics that decide whether that agent survives real volume. This whitepaper breaks down why the operations gap became an economics problem.

In this whitepaper, you'll learn:

• Why token consumption, not model choice, decides if AI ops pays for itself past the pilot
• Why Mean Time to Understand, not resolution speed, is the real bottleneck in production incidents
• How context engineering cuts cost and boosts accuracy at once, replacing the tradeoff most teams assume exists
• A buyer's framework for evaluating a production ops agent built to last past the demo

The practical takeaway for a CTO is a shift in the question. The question is not "which model is smartest," or even "which vendor has an agent." It is "who has engineered the context and economics so that autonomy is reliable, affordable, and defensible at the scale I actually run".

Every engineering org has pointed an AI agent at production by now. Few have engineered the context and economics that decide whether that agent survives real volume. This whitepaper breaks down why the operations gap became an economics problem.

In this whitepaper, you'll learn:

• Why token consumption, not model choice, decides if AI ops pays for itself past the pilot
• Why Mean Time to Understand, not resolution speed, is the real bottleneck in production incidents
• How context engineering cuts cost and boosts accuracy at once, replacing the tradeoff most teams assume exists
• A buyer's framework for evaluating a production ops agent built to last past the demo

The practical takeaway for a CTO is a shift in the question. The question is not "which model is smartest," or even "which vendor has an agent." It is "who has engineered the context and economics so that autonomy is reliable, affordable, and defensible at the scale I actually run".

Every engineering org has pointed an AI agent at production by now. Few have engineered the context and economics that decide whether that agent survives real volume. This whitepaper breaks down why the operations gap became an economics problem.

In this whitepaper, you'll learn:

• Why token consumption, not model choice, decides if AI ops pays for itself past the pilot
• Why Mean Time to Understand, not resolution speed, is the real bottleneck in production incidents
• How context engineering cuts cost and boosts accuracy at once, replacing the tradeoff most teams assume exists
• A buyer's framework for evaluating a production ops agent built to last past the demo

The practical takeaway for a CTO is a shift in the question. The question is not "which model is smartest," or even "which vendor has an agent." It is "who has engineered the context and economics so that autonomy is reliable, affordable, and defensible at the scale I actually run".