Product
Nubes Forge AI
Governed Agentic AI software delivery from requirements to production.
Nubes Forge AI coordinates specialized AI agents across the software delivery lifecycle — from requirements and planning to implementation, testing, security review and deployment.
Instead of generating code from a prompt and hoping it fits, Nubes Forge AI works inside defined architecture, quality, security and delivery controls. Every important output is tested against expected standards before it can move forward.
Go beyond vibe coding
From prompt-to-code to controlled software delivery
Coding is only one part of building production software. Requirements need to be understood. Architecture needs to remain consistent. Tests need to prove expected behaviour. Security needs independent checks. Changes need review. Releases need controls. And the next engineering team must still be able to understand and extend what was built.
Nubes Forge AI puts AI agents inside this complete delivery system. Agents can ask questions, develop specifications, plan implementation, write and review code, generate tests, resolve findings and prepare releases. Independent tools and deterministic rules decide whether the work meets the required quality and security standards.
Humans remain responsible for product intent, architecture, exceptions, risk acceptance and production authorization.
Why the system around the agent decides the outcome
DORA’s 2025 research on AI-assisted software development concludes that AI acts mainly as an amplifier of the engineering system already around it. Strong workflows become stronger; weak workflows can simply produce problems faster.
METR’s March 2026 review makes the same point from another direction: roughly half of the test-passing SWE-bench Verified pull requests written by recent agents would not have been merged by the repositories’ own maintainers. Passing a test is not the same as producing software an engineering organization wants to own.
That is the problem Nubes Forge AI is designed around.
AI can write the code. Engineering still has to control the result.
Agents can move fast. Their boundaries should move slowly.
Agentic delivery introduces a new engineering problem: prompts, models and agent behaviour can change much faster than the architecture and governance around them.
Nubes Forge AI therefore treats its AI configuration as part of the engineering system, not as settings someone adjusts in production.
Technology ecosystem
Nubes Forge AI is an orchestration and governance layer, not a replacement for the engineering tools your teams already trust. It connects agent models, work-management systems, source control, quality tools, security scanners and deployment platforms into one controlled delivery workflow.
Benefits
What Nubes Forge AI changes in software delivery.
The objective is not to maximize generated lines of code. It is to produce software another team can understand, test, operate and extend after the original delivery is complete.
How Nubes Forge AI works
Six stages across the lifecycle, with deterministic gates between them and human owners on the decisions that carry risk.
Understand
Requirements, designs, existing code and business context are collected and normalized. The system asks the questions needed to reach a defined readiness level.
Specify
Nubes Forge AI creates a versioned specification, architecture context, non-functional requirements, acceptance criteria, estimates and delivery tasks.
Build
Specialized agents implement bounded pieces of work using approved project patterns and isolated execution environments.
Verify
Tests, reviews, quality gates, security scanners and requirement traceability check the result independently. A failed mandatory gate returns the work for correction.
Approve
Human owners resolve business questions, architecture exceptions, security decisions and anything else that needs judgment.
Deliver
Approved changes pass through CI/CD, preview and release controls, with deployment evidence and delivery metrics retained for the client.
Measure the delivery system, not the number of prompts
Nubes Forge AI can measure both traditional engineering results and how AI contributes to them. That is a more useful claim than advertising millions of AI-generated lines.
What the market teaches us
The strongest products in this category are converging on the same ideas, which is good validation that the category is real. GitHub Copilot shows the importance of sandboxing, firewalls, rules and enterprise agent governance. OpenAI Codex shows that reliable agentic engineering needs a harness, sandboxing, feedback loops and orchestration — not only a strong model. AWS Kiro shows the move from prompt-driven to spec-driven development. GitLab Duo Agent Platform shows coding assistants growing into multi-agent lifecycle workflows, and Atlassian’s Rovo Dev connects work items and organizational context directly to implementation.
Model independence matters for the same reason. One model may be better at architectural reasoning, another at small implementation tasks, and a local model may be required for confidential code. Nubes Forge AI is model-aware rather than model-dependent: the workflow should survive a model change.
The product is not the agent. The product is the controlled system that makes multiple agents useful in real delivery.
Frequently Asked Questions
What is Nubes Forge AI?
Nubes Forge AI is the agentic software-delivery platform used by Nubes Consulting to coordinate AI-assisted work across requirements, planning, implementation, testing, security, review and deployment.
It combines specialized AI agents with deterministic quality controls, external engineering tools and human approval points.
Is this just another AI coding assistant?
No. Coding assistants mainly help developers write or change code. Nubes Forge AI operates at the delivery-workflow level.
It works from requirements and specifications through implementation, verification and deployment, and coordinates the different tools and roles required along the way.
Is Nubes Forge AI an autonomous developer?
No. Agents can operate autonomously inside defined boundaries, but autonomy is not the goal by itself.
High-impact product, architecture, security and production decisions remain controlled by the appropriate human owner.
Does it replace developers, business analysts and QA engineers?
No. It changes where their time is used.
Agents perform more of the repeatable analysis, drafting, implementation and verification. People focus more on intent, architecture, difficult decisions, exceptions and final accountability.
How do you stop AI-generated code from lowering quality?
AI output has to pass independent checks. Depending on the project these include automated tests, code-quality gates, security analysis, dependency scanning, secret detection, DAST, visual checks and human review.
The agent that generated the code cannot declare its own result acceptable.
Can Nubes Forge AI work with an existing application?
Yes. The workflow supports brownfield repository analysis as well as new development.
It can use the existing architecture, conventions, tests and codebase as project context, and run dedicated flows for feature development, defects and selected modernization work.
Can it work with GitHub, GitLab and Bitbucket?
Yes. Nubes Forge AI is designed to operate across common source-control and CI/CD environments rather than make the client move to one development platform. The exact integrations depend on the engagement.
Can we use our own AI model or provider?
Yes, where the model is compatible with the required workflow. Nubes Forge AI separates orchestration from model selection, so different approved providers or private endpoints can be used for different tasks.
This matters most where security, data residency, performance or cost rules restrict the available models.
Can it be used for regulated or confidential projects?
Yes, with an appropriate deployment model: project isolation, single-tenant execution, restricted egress, short-lived credentials, data redaction, private model endpoints, SSO and MFA, and detailed audit evidence.
Those controls support regulated delivery. They do not by themselves make an application compliant with every law or industry standard.
How do we know whether the AI is actually improving delivery?
We measure engineering outcomes rather than counting prompts or generated code.
Depending on the engagement we can compare delivery lead time, human effort, first-pass acceptance, defects, security findings, change failure rate, estimated versus actual effort and model cost.
Use AI to increase engineering capacity without removing engineering control
Nubes Forge AI helps our teams turn requirements into tested, secure and supportable software through a governed agentic delivery workflow. AI agents perform more of the repeatable work. Engineering standards decide what passes. People remain accountable for the result.
Turn your Technology Challenge into a clear Delivery Plan
Nubes Consulting Digital helps design, modernize and operate complex technology environments. From Cloud and Architecture to DevOps, SRE and Engineering Delivery, we focus on practical decisions, reliable execution and measurable business outcomes.
