AI holds a real place in how PI-GROUPE delivers, along two distinct lines: accelerating
software design, and orchestrating business processes through agents. Here is how — and,
more importantly, within which limits.
AI AS AN ACCELERATOR
AI-accelerated development
Large language models to speed up writing, review, documentation, testing and
refactoring, with human validation throughout.
Code · Tests · Review · Docs
Augmented architecture
Scenario exploration, architecture decision records, documentation and constraint
analysis with AI assistants.
Architecture · ADR · IaC · Security
LLM workflows
Automated collection, transformation, classification, generation and hand-off
towards enterprise systems.
Agents · RAG · APIs · Workflow
Principle: AI accelerates engineering; architecture, security and
production decisions stay governed and traceable.
Line 1
Designing and building alongside AI
Assisted programming changes how you approach an architecture problem. Where teams once
settled for the first workable answer for lack of time, they can now weigh three.
Exploring options
Before settling an architecture, generate and compare several variants with
their cost, operational and security implications. The decision stays human, but it rests
on a wider field.
Infrastructure code generation
Terraform modules, Ansible roles, Helm charts, Go operators. Everything
generated goes through review and testing: the model proposes, the engineer validates.
Test coverage
Generating Bats, Molecule, pytest or Helm unittest scenarios. This is the
best gain-to-risk ratio of the lot: a generated test that fails shows up immediately.
Living documentation
Documentation written during development rather than six months later.
Design documents, runbooks, decision records.
What AI does not do. It does not decide an architecture, does not know your
regulatory constraints and carries no responsibility for the outcome. Generated code passes
the same quality gates as hand-written code: review, tests, static analysis, vulnerability scan.
Line 2
Automating processes with supervised agents
An agent is a language model given tools, a memory and a scope. Orchestrated properly, it
absorbs the repetitive work that otherwise ties up scarce skills.
Agents are specialised and check one another. Error recovery, logging and guardrails are designed in from the start rather than bolted on later.
Document flow processing, structured extraction, synchronisation between
systems that do not talk to each other, conditional follow-ups and notifications.
Project use cases
Targeted technology watch, tender analysis, preparing design documents from
an existing documented estate.
Guardrails
Four non-negotiable rules
Every irreversible action goes through a human
Deletion, payment, outbound sending, production change: the agent prepares,
the human approves. The boundary is set during framing, not improvised later.
Every run leaves a usable trail
Input, reasoning, tools called, output, decision. An automated process you
cannot audit is one you cannot fix.
Sensitive data stays inside the perimeter
Model choice and hosting follow the data classification. For constrained
environments, self-hosted models and network segmentation.
An agent has a scope, not blanket access
Dedicated service accounts, least privilege, call quotas. The same
principles as for any other component of an information system.
A process to automate, a team to equip?
The first job is telling apart what benefits from automation and what should stay human.
That is often the most valuable part of the engagement.
