Software factory for multi-agent systems

A team of agents that autonomously deliver production‑ready changes for you.

Prinevo.ai gives your team agents that turn requests into validated, production-ready changes with shared context, coordinated work, and evidence before release.

Bring Codex, Claude, Cursor, and custom agents together to turn requests into validated changes across product, code, tests, review, and rollout.

Request Early Access See how it works
Works with CodexClaudeCursor GitHubSlackLinear
Part of Daytona Startup Grid Daytona Startup Grid
Faster project deliveries Shared context and coordinated agents reduce handoff waste.
Changes Validated in Sandbox Tests, screenshots, logs, contracts, review, and rollout proof travel with the work.
One flow Remember, plan, coordinate, build, verify, and learn from every production-ready change with shared context carried through the entire flow.
Long-running tasks Agents stay on multi-step work across repos, verification, review, and rollout.
The Delivery Gap

Agents speed up code. Teams still have to make it shippable.

Today, senior engineers still supply the missing multi-agent system context, coordinate the work across repos and teams, and assemble the proof reviewers need before release.

GAP 01

Context Is Spread Across Systems and Lost After Run

Agents need unified, model-agnostic context across architecture, ownership, service contracts, customer impact, infra, decisions, and rollout history before they can make the right change.

GAP 02

Coordination Is the Bottleneck

Product requirements, frontend, backend, workers, infra, tests, reviews, and deployment order still have to line up across teams and repositories.

GAP 03

Trust Has to Be Earned

Engineering teams still assemble proof manually across code review context, test results, logs, screenshots, monitors, rollback path, PR context, and the audit trail of what each agent did and why.

GAP 04

Control Is Fragmented

Model choice, usage, cost, access, policies, and approvals are spread across tools instead of governed from one delivery control plane.

Technical Architecture

One governed loop from request to verified change.

A shared context layer, governed models and tools, and verified learning connect the full delivery loop.

Prinevo delivery system Live stage walkthrough
Human + Agent Collaboration
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
Controlled learning loopOnly validated updates influence future runs.
Completed runEvidence + reviewer decisions
Promotion gateValidate source, scope, freshness
Next runMemory + checks start stronger
Platform

Give every agent the context, controls, and proof it needs to ship.

Your team sets the outcome. Prinevo gives every agent the same context layer, coordinates work across repositories, validates production-ready changes with evidence, and keeps the software factory steerable while work is in flight.

01 Context foundation

Multi-Agent System Context Layer

Build common, reusable context for every model and agent across product behavior, customer impact, workflows, repositories, owners, contracts, infra, decisions, incidents, rollout history, and learnings.

Prinevo memory benchmark

Evaluated across established long-context memory benchmarks.

LongMemEval82.60%
LoCoMo83.03%
What your team gets
  • Reusable contextShared memory follows every model and agent.
  • Better with every runValidated decisions, evidence, and fixes improve the next run.
02 Collaboration foundation

Human and Agent Collaboration

Teams set direction while specialist agents plan, build, and coordinate compatible changes across product, backend, frontend, workers, data, infra, review, and release.

What your team gets
  • Multiple agentsArchitect, Data, QA, Code Review, and specialist agents work as one team.
  • Multi-repo deliveryCoordinate owners, services, dependencies, and release paths.
  • Long-running workKeep multi-step tasks moving through review, verification, and rollout.
  • Human steeringGuide work in flight and intervene when direction changes.
03 Proof foundation

Sandbox-Tested and Ready for Review

Run the change in a sandbox and package seed data, test reports, logs, screenshots, contract results, rollout evidence, audit trails, rationale, and PR context so reviewers can approve with confidence.

What your team gets
  • Validated in a sandboxTests, screenshots, logs, contracts, seed data, and rollout proof travel with the change.
  • Ready for reviewReviewers receive the change, rationale, action trail, and proof together.
04 Governance foundation

Governance Control Plane

Choose models, grant stage-specific tools and access, manage usage and cost, enforce policies, and keep delivery governed from one place.

What your team gets
  • Model choicePick the best approved model for each agent stage.
  • Scoped accessGive each agent only the tools, repositories, and environments it needs.
  • Policy and cost controlManage usage, limits, approvals, and auditability centrally.
Software factory metrics

Long-running agents, stronger memory, faster delivery.

Prinevo gives your team a software factory that can keep agent work alive across long tasks, reuse the right context, and move projects through verification faster.

12+ hours Agents can work through long-running tasks across repositories, reviews, verification, and rollout without losing context.
64.7% In-domain retrieval accuracy with Prinevo memory, up from 23.5% in evaluation.
50%Faster project deliveries Faster project delivery from coordinated agent work, reusable context, and fewer handoff gaps.
Operating Model

Plan and build sandbox-validated, production-ready changes. Build features autonomously.

Remember Plan Coordinate & Build Verify Learn
01

Remember

Integration Memory

Integrations connect Slack, GitHub, Linear, docs, incidents, and repos so decisions, ownership, contracts, rollout constraints, and customer context become reusable memory for every model.

02

Plan

Product Agent Architect Agent

Product Agent turns the request into scope and acceptance criteria while Architect Agent maps affected systems, risks, model choice, access needs, and the verification path before code changes start.

03

Coordinate & Build

Lead Agent Implement Agent Data Agent Infra Agent

Lead Agent sequences long-running work while Implement, Data, and Infra Agents make compatible changes across product, backend, frontend, workers, schemas, verification, and release with scoped permissions. Teams can steer the agents at any point to clarify intent, adjust scope, or redirect the work.

✓ access scoped
04

Verify

QA Agent Code Review Agent

QA Agent brings services up in a sandbox, loads seed data, runs API and contract tests, captures screenshots, inspects logs, and hands evidence to Code Review Agent for reviewer-ready proof.

05

Learn

Learning Agent

Learning Agent stores useful facts, failed checks, reviewer decisions, rollout notes, and workflow improvements back into memory so the next agent run starts smarter.

+ gates improved
Factory - Your team of agents all agents healthy
Prinevo agent workspace showing sessions, prompt composer, artifacts, and verification controls.
Why a Software Factory

Coding agents are workers. The factory is the delivery system.

Codex, Claude, Cursor, and custom agents can produce code quickly. The software factory adds the multi-agent context layer, governance, and verification path that make each change coordinated, verified, and ready for review.

Coding Agents Alone
Software Factory
Engineering Context
Works from the repo, prompt, and visible files in the current session.
Uses Organizational Memory across product behavior, customer impact, owners, contracts, infra, incidents, decisions, and rollout history.
Cross-Repo Work
Creates local changes, but dependencies across product behavior, frontend, backend, workers, and infra still need manual coordination.
Coordinates the Outcome across product requirements, affected repos, compatible contracts, sequencing, owners, and release order.
Verification
May run local tests, but integration proof, screenshots, logs, and contract results are usually assembled by engineers.
Packages Evidence with tests, logs, screenshots, contract checks, review notes, and PR summary in one place.
Rollout Readiness
The deploy order, monitors, rollback path, and release risk still live in team knowledge.
Plans the Release Path with rollout order, monitors, rollback path, and reviewer-ready decision context.
Vendor Lock-In
Teams often standardize around one agent or model workflow, so delivery memory stays tied to that tool.
Uses Any Coding Agent or Model so Codex, Claude, Cursor, custom agents, and future models can work inside the same delivery system.
Control Plane
Model selection, usage, cost, access, and approvals are usually managed separately across tools and teams.
Governs Models and Agents by selecting the right model for each task, tracking usage and cost, enforcing access policies, and routing approvals.
Learning
Each session can start fresh unless a human carries forward what failed last time.
Improves Every Run as new facts update memory, repeated manual work becomes workflow, and failed checks become gates.
Delivery Package

Ask for the outcome. Get coordinated changes, checks, and a rollout path.

PLAN

Map the affected repos, owners, contracts, and deployment order.

The factory retrieves context from integrations, Organizational Memory, connected tools, similar failed changes, deployment constraints, and the checks that proved the path last time.

BUILD

Multi-agent coordination for compatible changes across product, frontend, backend, workers, and infra.

Specialist agents map product behavior, expand the API safely, update the worker, sequence infra, and connect the frontend behind a rollout flag.

PROVE

Package sandbox-verified, reviewer-ready output.

Sandbox test reports, runtime logs, screenshots, contract notes, rollout order, monitors, rollback path, and new memory facts are packaged with the PR.

prinevo / delivery-report — verified
outcome  chat-based data analysis
repos    4, owners 6
plan     ok deployment order confirmed
build    ok product, frontend, backend, infra coordinated
review   ok code review before rollout
verify   ok tests, logs, screenshots, contracts
rollout  ok monitors and rollback path set
lessons  ok captured for next run
● status: ready for reviewer decision
Integrations

Connect the systems where delivery context already lives.

Connect GitHub, GitLab, Linear, Jira, Slack, Notion, Google Drive, CI, cloud, observability, incident management, and deployment systems so the agent fleet works from the same engineering context your team already uses.

⌘
Codex
AI Coding Agent
✳
Claude
AI Coding Agent
▮
Cursor
AI Coding Agent
⚙
Custom Agents
Any agent via API
⎇
GitHub
Repos, PRs, CI
◫
Linear
Issues and Planning
#
Slack
Team Coordination
DT
Datadog
Logs, Metrics, Traces
+
100+ More
GitLab, Jira, Notion, Drive, CI, Cloud, Observability
Beyond Code Changes

Once the factory has context, it can support the rest of engineering.

Cost, reliability, security, and compliance issues all connect back to code, infrastructure, ownership, runtime behavior, deployment history, and customer impact.

Example Investigation Why did data warehouse costs spike last month?
Evidence

Cost increased 28% after the analytics worker deployment.

Trace

Correlate spend, deployments, logs, traces, and query metrics to one repository, module, and path.

Action

Find the root cause, estimate savings, make the code change, and open a PR with validation evidence.

Production-Ready Code Changes

Produce validated production-ready changes with architecture notes, coordinated repo updates, review context, test evidence, rollout plan, and a PR ready for deployment.

Cost Analysis

Connect cloud spend, deploy history, metrics, queries, workers, repos, and owners.

OPS

Reliability Diagnosis

Trace incidents to code paths, rollouts, monitors, service contracts, and regression tests.

SEC

Security and Compliance

Review changes against data flows, policies, audit needs, ownership, and release readiness.

Need help with setup or want us to run this for you? We offer FDE support as well.

Request Early Access

Request Early Access to Prinevo.

Prinevo is for engineering teams using coding agents in production workflows. Bring a multi-repo change, verification flow, or rollout package you want to turn into a validated production-ready change.