Agentic Software Engineering Consultancy
Engineering's bottleneck was never typing code — it's architecture, context and review. Autonomous agents removed the execution bottleneck. We execute for you, or we train your team to execute. Either way, the result is measured in your own repository.
The new paradigm
For decades, an engineering team's delivery capacity was limited by the number of people able to write and review code. That constraint is gone. Leading companies already operate with lean teams multiplied by agents — and the question is no longer whether your engineering will adopt this model. It's who will lead it.
Autonomous agents plan, implement across multiple files, write tests, open Pull Requests and fix CI — in parallel and without line-by-line supervision. What used to be the center of engineering work became a commodity.
The engineer left the execution loop and entered the direction loop: deciding what to build, defining the context agents operate in and designing the quality system that validates the result.
Increasing delivery capacity is no longer a function of headcount. It became a function of how many agentic streams technical leadership can direct, govern and audit safely.
The thesis
The HIC is the engineer whose impact is no longer limited by their own execution capacity. They act as architect and tech lead of a team of autonomous agents that covers the full software lifecycle: specs, design, implementation, testing, code review, documentation, deploy and maintenance.
This is why 1 or 2 HICs sustain workloads that used to require entire squads: execution scales with orchestration, not with hiring.
An HIC is not the "10x engineer" who types faster. It's a new structural role: the tech lead whose team is mostly made of agents.
Deciding what to build, the system design and the trade-offs. Agents execute very well within a well-defined context — defining that context is high-level human work, and it's the HIC's first differentiator.
The new "knowing how to code": writing specs, standards and system knowledge so that agents implement it right the first time. The spec became the contract; code became the consequence.
Defining quality gates, permissions and where human review is non-negotiable. The HIC doesn't review every line — they design the quality system that reviews for them.
How it works
This isn't "using AI to autocomplete". It's a pipeline where each SDLC stage has a responsible agent, an input contract and a quality gate on the way out — with human decision points defined by design, not improvised.
The requirement becomes an executable spec: scope, acceptance criteria, repository standards and architecture constraints.
Human directionThe agent breaks the spec into an implementation plan, maps the impacted files and surfaces risks before writing code.
AgentParallel multi-file execution, following the repository's own standards and the project's spec library.
AgentTests generated by default alongside the feature — not as debt to be paid later. If it fails, the agent fixes it and runs again.
AgentQuality gates, adversarial review by agents and security checks before any human eyes are spent.
AgentPR opened with context and traceability; the agent follows CI and fixes whatever breaks until the pipeline is green.
AgentThe HIC decides what is non-negotiable for human review: sensitive changes, security surfaces and architecture decisions.
Human directionDocumentation updated as a by-product of the flow, governed deploy and maintenance with triage and fix agents.
AgentWhat changes in practice
| Metric | Traditional squad | HIC model |
|---|---|---|
| Feature lead time | Weeks | Hours |
| Code review | Senior engineers' bottleneck | Automated with quality gates |
| Tests and documentation | Always behind | Generated by default in the pipeline |
| Senior capacity | Consumed by manual review | Freed up for architecture |
| Delivery scale | Hire more people | Orchestrate more agents |
Tests and documentation stop being the first thing cut under deadline pressure.
Quality gates applied consistently across every delivery, not only when there's time.
The company's most expensive capacity goes back to architecture and decision-making, not manual review.
Scaling no longer depends on hiring, training and waiting for new people to ramp up.
Two ways to work with us
There's no single right answer for every company — there's the right answer for your moment. If the pressure is to deliver now, start with execution. If the goal is to change your engineering permanently, start with training. Many do both, in that order.
Execution · we deliver
"Want us to migrate your legacy or ship your ideas?"
A squad of autonomous agents led by a NextLearn HIC takes on the work and hands it back running in production.
Training · your team delivers
"Want your engineering to start operating this way?"
An intensive immersion plus guided implementation, with a real pilot running inside your own repository.
Agentic Squad with a Guarantee
Every tech company carries the same two things: a list of ideas that never leaves the page and a legacy system nobody wants to touch. Years of meetings, prioritization and roadmaps solved neither — because the problem was never willingness. It was execution capacity.
An agentic squad doesn't have that limit.
"Only one person understands that system. No tests, no documentation, and every new delivery runs into it. Rewriting is too expensive and too risky."
Agents read the entire system — not a sample — rebuild the documentation that never existed, cover current behavior with characterization tests and only then migrate, piece by piece and with the system live. Knowledge leaves one person's head and becomes a company asset.
"The idea is validated, the customer wants it, the business needs it — but there's no one available. It's been in the backlog for three quarters waiting for someone to free up."
New product, integration, automation, that module that never had a team: from spec to deploy with the same governance as the HIC model. You follow progress in Pull Requests in your repository, not in status slides.
We look at the code, the environment and the real problem. You walk away with a technical assessment — even if you decide not to proceed.
What "done" means is written and agreed before we write the first line. That document is what the guarantee covers.
The squad runs the full pipeline — plan, build, test, review, PR — under an HIC's direction and the agreed quality gates.
In production, tested and documented. Your team gets the full handover and takes over whenever it wants.
We define the acceptance criteria together before we start. If the delivery doesn't pass those criteria, we redo it. If it still doesn't pass, you don't pay for it. The execution risk is ours — not yours.
Scope, timeline and commercial terms are defined per project, at the diagnosis — because guaranteeing a result requires understanding the problem before promising anything.
It goes through the same quality gates we design in the HIC model: tests, adversarial review by other agents and security checks, defined together with you before we start. What doesn't pass the gate doesn't become a Pull Request. And what does, an HIC signs off on.
We operate inside your environment, with the permissions you grant and a full audit trail of everything done. You define where human review is non-negotiable — and nothing gets past those points without approval.
Old systems are exactly where the gain is largest: the cost of understanding the code is what stalls these projects, and that's precisely what agents do fast. We start with a small, verifiable slice, so you can see the method working before scaling up.
That's the best possible outcome — and the path already exists: your managers join the HIC program and start operating the same model. We don't live on dependency, we live on results.
If it's not for us, we'll say so. If it is, you get the scope and acceptance criteria before taking on any commitment.
HIC Program · training
A 10-week program for cohorts of up to 6 managers, starting within 2 weeks of signing. It's not a course: by the end of week 2 your team already operates the HIC model, and the following 8 weeks exist to secure the result inside your own environment.
The whole method delivered in 4 live 3-hour sessions.
Why engineering's constraint moved, what defines the High Impact Contributor and how to reposition the team around that role.
Code agents, MCP, spec engineering and integration with Git and CI/CD — the stack that sustains real agentic operation.
plan → implement → test → review → PR: how to build, measure and debug the flow where agents deliver with real autonomy.
Permissions, auditing, where human review is non-negotiable — and defining the pilot with the baseline to be measured.
The method leaves the classroom and enters the company's repository.
What's measured at the end is what was really happening in your code — not a training simulation.
The method documented and adapted to your engineering's context, stack and constraints.
Specs, standards and agentic workflow templates reusable by the teams from the next day on.
The quality gates, permissions and human review criteria that make autonomy safe.
An agentic flow operating in your environment, with a result measured against the initial baseline.
The path to take the model from the first squad to the rest of the organization.
Playbook, templates and mastery of the method stay permanently in the company — they don't depend on the consultancy.
Who it's for
When it's not
Who leads it
Founded in 2023, NextLearn was born to create education solutions and digital environments that use Artificial Intelligence to solve complex problems with real impact. Today we apply that same repertoire inside engineering teams: turning software teams into high-performance agentic operations.
A Computer Scientist specialized in AI, Cybersecurity and high-scale architectures, with over 20 years of experience in digital transformation. He founded Flexa Cloud — a company specialized in cloud computing, Big Data and AI, and an AWS Advanced Partner — after a career in large corporations.
Specialized in Generative AI at MIT, he serves as an advisor and executive helping companies transform industries through Artificial Intelligence.
Discover Deivid Bitti's work and publications deividbitti.com
Deivid was the cover of CIO Review LATAM, a publication with hundreds of thousands of subscribers in the United States and Latin America, in an edition about building intelligence that moves industries.
In the same edition, Flexa Cloud was recognized as Top AWS Generative AI Services in Latin America 2025, after evaluation by a panel of C-level executives and the magazine's editorial board.
Next step
A conversation to understand your stack, your problem and where the real bottleneck is. From it, you get the delivery slice and the acceptance criteria — before taking on any commitment.