BECOME NOW™ · TECHNOLOGY & ENGINEERING

Build the judgment required to work with LLMs, agents and AI systems.

A program for technical teams making stronger architecture, development, evaluation, integration and operating decisions for AI capabilities.

THE USUAL PROBLEM

A demo works long before a dependable system exists.

The prompt responds and the agent acts. Then context, evals, permissions, latency, cost, observability and ownership appear. The challenge is not calling a model; it is designing a capability that fails visibly and safely.

WHO IT IS FOR

Software Engineering
Architecture
QA
DevOps
IT Operations
Technology Leadership

BEFORE THE CURRICULUM

Session 0 defines one technical case and its operating constraints.

We review architecture, repositories, APIs, knowledge, environments, security, SLAs, costs and standards. Evaluation data, expected behavior, threats, observability and human review are agreed.

RECOMMENDED JOURNEY

Build the judgment to work with LLMs, agents and AI systems.

Session 1: Foundation models and prompting

What stays installed: Engineering Copilot with review criteria.

Session 2: Context engineering, RAG and embeddings

What stays installed: Grounded technical assistant.

Session 3: APIs, tool calling and MCP

What stays installed: Workflow that consults systems instead of guessing.

Session 4: Agentic workflows and routing

What stays installed: Orchestration with limits and approval.

Session 5: Evaluations, guardrails and testing

What stays installed: Eval suite and release criteria.

Session 6: Observability, incidents and cost

What stays installed: Runbook with tracing and ownership.

HOW EACH SESSION WORKS

Every capability is tested for expected behavior, failure and misuse.

The team documents architecture, dependencies, threats, evaluation data and thresholds. Correctness, consistency, security, latency, cost and recoverability all matter.

TECHNOLOGY IN YOUR ENVIRONMENT

The journey adapts to approved models, clouds, frameworks and standards. Labs use controlled environments; production deployment requires an implementation scope.

POSSIBLE DELIVERABLES

What the team takes away.

Engineering Copilot and usage policy.
Context engineering or RAG design.
Tool calling / MCP blueprint.
Agentic Workflow Blueprint™.
Evaluation dataset and initial suite.
Observability, incident and cost runbook.

BEFORE YOU DECIDE

Measures may include eval pass rate, groundedness, defects caught, latency, cost per task, human intervention and incidents.

FREQUENTLY ASKED QUESTIONS

What teams in this area actually ask.

Is this a coding bootcamp?

No. It combines practice and engineering judgment for enterprise AI.

Is it tied to one provider?

No. It adapts to and can compare the approved stack.

Are agents included?

Yes, when the use case justifies tools, autonomy and controls.

Does the result go to production?

Not necessarily. Production belongs to an implementation engagement.

OTHER PROGRAMS

Is another area the one that needs to start?

Product, Data & Technology

Product & Innovation

Data & Analytics