Prinevo turns a request into a verified change by connecting five parts:
shared context, a specialist agent team, governance, sandbox validation,
and learning that improves the next run.
Prinevo delivery systemLive stage walkthrough
InputProduct request
Retrieving reusable context
OutputVerified change
Human + Agent Collaboration
Set directionSteer in flightApprove key gates
Stage 01 of 05
Assemble the context for this change.
Team owner + Memory Agents
Prinevo retrieves the relevant decisions, owners, contracts, incidents, and prior run evidence instead of asking every agent to start from a blank prompt.
Context inLive sources and approved organizational memory
ControlScoped read access, freshness, and allowed use
OutputBounded context bundle with source pointers
Stage 02 of 05
Turn the request into an executable plan.
Product owner + Product & Architect Agents
Product Agent defines scope and acceptance criteria. Architect Agent maps affected systems, multi-repository dependencies, risks, access needs, and the verification path.
Context inRequest, ownership, decisions, contracts, and constraints
ControlStage model, cost limit, and human scope gate
OutputApproved plan, acceptance criteria, and dependency graph
Stage 03 of 05
Coordinate specialist agents across repositories.
Tech lead + Lead & Specialist Agents
Lead Agent sequences the work while specialist agents make compatible backend, frontend, data, worker, and infrastructure changes without losing shared decisions.
Context inApproved plan, repository conventions, and shared contracts
OutputCompatible multi-repo changes and an action trace
Stage 04 of 05
Prove the change in a sandbox.
Reviewer + QA & Code Review Agents
Independent verification checks the requested behavior and packages inspectable proof before a reviewer or release owner is asked to approve the change.
Improve the next run without uncontrolled self-learning.
Domain owner + Learning Agent
Completed evidence, reviewer decisions, failed checks, and rollout results become candidates for reusable memory, stronger regression coverage, or workflow improvements.
Agents start with the engineering brain your team has already built:
architecture, decisions, repository maps, owners, contracts, prior incidents,
accepted patterns, and evidence from past work.
Approved memory and source pointers
Repo and service understanding
Freshness and scope controls
02
Specialist agent team
Product, Architecture, Lead, Implement, Data, QA, Code Review, Deploy Plan,
and Learning agents work as one delivery team instead of isolated coding sessions.
Right model for each stage
Right harness for each job
Shared state across the run
03
Governance plane
Teams decide where agents can act autonomously and where people need to approve,
retry, revise scope, or steer the work before it moves downstream.
Model, tool, access, and cost policy
Configurable approval gates
Audit trail for agent actions
04
Sandbox validation
The change is checked in an isolated environment and packaged with proof before
reviewers or release owners are asked to trust it.
Tests, logs, and screenshots
API and contract checks
Rollout and review evidence
05
Learning loop
Validated evidence, reviewer decisions, failed checks, and repeated manual work
become candidates for stronger memory, better skills, and new specialist agents.
Reviewed memory updates
Reusable skill promotion
Better future runs
Control plane
Autonomy increases only where the system has proof.
Small, low-risk changes can pass through lightweight gates. Meaningful changes can
require review at PRD, HLD, LLD, implementation, verification, or release. The goal is
not to slow agents down. The goal is to catch bad direction before it becomes expensive.
Models
Route by job.
Use deeper reasoning for planning and architecture, and faster models for narrow build or validation tasks.
Access
Scope every run.
Give each stage the repositories, tools, connectors, and environments it needs for the active task.
Evidence
Package proof.
Keep tests, logs, screenshots, contract checks, rollout notes, and review context with the change.
Learning
Improve deliberately.
Promote only reviewed facts, skills, and policies into future runs so learning stays controlled.