For engineers, architects, tech leads and engineering leaders who want to turn individual coding-agent use into a more structured, verifiable and reusable delivery system — from context and specification to code, tests, release and operations.
You work on a real technical problem across the 12 weeks and leave with a demonstrable deliverable, quality evidence and an adoption plan — not disconnected exercises.
12 weeks · 2 tracks · applied project on an existing codebase · technical practice with supervision
You can join alone and apply the project at your current job, or bring the team to turn individual productivity into shared engineering capability.
For technical professionals who want to move beyond experimental AI use and build a more mature way of working with context, specification, agents, testing and operations.
For organizations where developers already use AI, but productivity remains individual, inconsistent or hard to measure.
The program answers different problems depending on the role. Explore what each profile learns and builds — the real technical problem remains the common core.
Move from one-off productivity to a reproducible delivery workflow.
See problems, learning and deliverable
Problems
You learn
You build
Ensure agents respect architecture and prior decisions.
See problems, learning and deliverable
Problems
You learn
You build
Make model integrations robust, evaluated and observable.
See problems, learning and deliverable
Problems
You learn
You build
Define execution boundaries and standardize CI/CD and observability.
See problems, learning and deliverable
Problems
You learn
You build
Turn individual adoption into consistent team practice.
See problems, learning and deliverable
Problems
You learn
You build
Decide where AI improves engineering economics without shifting cost.
See problems, learning and deliverable
Problems
You learn
You build
You do not need to arrive wanting a specific tool. Bring the real bottleneck: context, review, brownfield, testing, CI, agents, cost or adoption.
Your problem is not on the list? Bring it anyway.
Coding assistants lowered the cost of producing code. The new bottleneck is providing context, specifying correctly, reviewing, testing, integrating and operating changes without increasing rework and risk.
An agent can write hundreds of lines quickly and still not understand the architecture, prior decisions or system constraints.
If generation outpaces review and testing, the bottleneck simply moves.
Each engineer building their own workflow can increase local speed without improving team delivery capacity.
The idea starts from High Individual Contributor: professionals who use AI to expand their capacity to analyze, decide, build and deliver. At EBAC, we believe this only truly works with Human in Control: people keeping judgment, evidence and responsibility for what AI does.
More capacity to analyze, decide, build and deliver with AI.
People keep judgment, validation, evidence and responsibility.
Apply the method to a relevant problem, product, service or opportunity, with mentoring.
More capability with AI. People stay in control.
Every synchronous HIC program starts from a real problem. The domain changes; the method core does not: learn only what is needed to build, verify, and prepare application and transfer with human judgment.
Define the problem, people, baseline, constraints, expected value, and success criteria before choosing technology.
Technical and methodological depth follows the challenge and participant role — not a fixed tool checklist.
Turn learning into a solution, workflow, analysis, prototype, artifact, or decision that fits the context.
Test quality, evidence, risks, and limits while keeping review, accountability, and Human in Control.
Document, define ownership, and prepare next steps so the learning can be reused and improved beyond one person.
Implementation does not automatically mean production deployment: each context keeps its own security, approval, integration, and decision gates.
Two tracks for the same problem: who builds and who defines criteria, quality and rollout. In a corporate cohort, both can work the same challenge.
For those who build or modify systems — context, spec, architecture, agent, code, tests, security, pipeline and observability.
For whom
Capabilities
What you build
For those accountable for architecture, people, quality, risk, cost or delivery capacity — problem, criteria, decomposition, delegation, review, gates, economics, operating model and rollout.
For whom
Capabilities
What you build
The tracks are not two isolated courses: in a corporate cohort, Builder builds and verifies the pilot while Technical Leadership defines criteria, operating model, economics and rollout for the same problem.
The value also changes by engineering organization type. Explore what each context faces, develops and can build.
Reproducible AI-assisted SDLC between feature delivery and quality.
See context and application
What is usually blocked
What the program develops
What can be built
Legacy modernization and automation with guardrails.
See context and application
What is usually blocked
What the program develops
What can be built
Reusable standards and consistent delivery across projects.
See context and application
What is usually blocked
What the program develops
What can be built
Just enough structure without destroying speed.
See context and application
What is usually blocked
What the program develops
What can be built
Internal tools, standards and guardrails across squads.
See context and application
What is usually blocked
What the program develops
What can be built
The Shared HIC Method Core is the common method across programs. In engineering, it materializes into a specific delivery flow — from problem to institutionalized capability.
What problem is really worth solving?
What must happen and how will we know it is right?
What changes in the system and what trade-offs exist?
What should be done by person, agent or tool?
What evidence proves the change is acceptable?
How to observe, revert and maintain?
How does the practice stop depending on one person?
12 weeks with practical deliverables applied to the vertical context.
Week 1
Identify a real development-cycle problem and measure how it works today (baseline).
Topics
Lab
Delivery problem brief + current workflow measurement
Validation criteria
Week 2
Build a disciplined way of working with AI in the development cycle.
Topics
Lab
Work harness applied to the problem
Validation criteria
Week 3
Build and maintain rich context for LLMs: codebase, docs, dependencies and decisions.
Topics
Lab
Project context pack
Validation criteria
Week 4
Specify outcome, acceptance criteria and tests before building.
Topics
Lab
Specification sprint with peer review
Validation criteria
Week 5
Decompose the problem into modular architecture with ADRs and tasks appropriate for humans/agents.
Topics
Lab
ADRs + technical backlog
Validation criteria
Week 6
Orchestrate coding agents with human supervision and clear criteria.
Topics
Lab
Agent-assisted functional change
Validation criteria
Week 7
Apply AI to an existing codebase or representative brownfield environment safely.
Topics
Lab
Safe change in existing codebase
Validation criteria
Week 8
Integrate models and tools as the problem requires — MCP/RAG are adaptive, not mandatory.
Topics
Lab
Functional integration per the problem
Validation criteria
Week 9
Create quality gates with tests, evals and security checks.
Topics
Lab
Quality gates with human review
Validation criteria
Week 10
Automate CI/CD with pipelines, release and observability.
Topics
Lab
Pipeline + telemetry
Validation criteria
Week 11
Measure cost/latency and define the team operating model.
Topics
Lab
Project operating model
Validation criteria
Week 12
Consolidate the verified pilot and the organizational adoption roadmap.
Topics
Lab
Demo and rollout preparation
Validation criteria
Coordination of the AI software development program at EBAC Business.
Alan Dantas works at the intersection of software architecture, applied AI, and delivery governance — leading EBAC Business executive programs and practical labs.
In the program, the emphasis is on executable artifacts (PRD, specs, ADRs), agent orchestration, quality gates, and observability — not isolated prompts.
The program ends with Demo Day featuring a verifiable pilot with evidence and a 30-60-90 roadmap, preparing squads for organizational rollout.
Does not promise full team replacement. Human supervision, secret security, and supply chain are explicitly addressed in each module.
EBAC has been training market professionals for years: we know which skills the market is demanding right now — and where many teams still get AI wrong.
Executive applicationAI as a leadership and decision topic, not just a technical tool.
Team in the fieldHands-on workshops with professionals from leading companies.
Governance and cultureBuilding criteria, not just experimenting — from adoption to control.
Real executionMixed cohorts of HR, leaders and specialists apply in practice.
Fill out the form below and our team will contact you.
Corporate cohorts
Talk to our team about in-company implementation and dedicated cohorts.
Still have questions? Talk to our team.
Chat on WhatsAppYou do not need to arrive with the tool already chosen. Bring the real bottleneck. The program helps structure the problem, design the workflow, build a verifiable pilot and decide what deserves to be adopted by the team.
