Software factory for multi-agent delivery.

A team of self-learning agents that delivers verified production changes.

Prinevo is a software factory for multi-agent delivery. It gives engineering teams a coordinated team of agents that works in cloud workspaces with shared organizational context, spans teams and repositories, validates changes in sandboxes, and returns evidence before release.

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Works with CodexClaudeCursor GitHubSlackLinear
Part of Daytona Startup Grid Daytona Startup Grid
Built by a team with experience at
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.
Bring your own subscriptions Connect your existing Claude, Codex, Cursor, and other model subscriptions, then use the right model for each agent and task.
Automate work from your tools Start work from Slack or turn approved Linear bug tickets into sandbox-validated fixes automatically.
Keep delivery moving 24/7 Cloud agents continue approved work when your team is offline, so long-running tasks keep progressing.
The Delivery Gap

The gap is no longer writing code. It is controlling delivery.

Coding agents create more work faster. Your team still has to align owners, protect architecture, choose the right model, manage cost, review the right gates, and prove each change is safe to ship.

GAP 01

Agents do not know how your company ships.

They do not know your architecture decisions, service contracts, owners, rollout rules, customer impact, or what worked last time. Engineers end up putting that context back together for every run.

GAP 02

Senior engineers become the coordination layer.

One feature can touch product, frontend, backend, data, infra, tests, review, and release. When agents work in isolated sessions, people still coordinate owners, dependencies, and handoffs.

GAP 03

Validation and PR review happen too late.

Every feature needs test cases, QA validation, sandbox evidence, and PR review before it ships. When those checks start only after code is written, bad PRD, HLD, LLD, or implementation choices become expensive rework.

GAP 04

Model choice and cost are hard to govern.

Different tasks need different models, reasoning levels, and budgets. Without one policy and cost view, teams overuse expensive models, underpower important planning work, or lose track of spend.

GAP 05

Access control and audit trails are scattered.

Agents touch repos, tools, environments, and release paths. Teams need scoped access, approval gates, and a clear audit trail showing what each agent did, which tools it used, and who approved key steps.

Operating Model

Plan and build multi-repo, sandbox-validated, production-ready features.

Shared engineering context One delivery system that gets smarter with every run.

Agents use the context your company already has, coordinate a complete change, verify it in a sandbox, and return the useful lessons to the next run.

What agents start with
Architecturesystems and contracts
Decisionsowners and constraints
Work systemsSlack, Jira, GitHub, docs
Prior evidencetests, reviews, rollouts
The delivery run
01Context graphRight context for the task
02PlanScope, design, and gates
03Coordinate & buildSpecialists work in parallel
04VerifySandbox proof before release
05LearnMemory, skills, and stronger gates
Human steeringApprove key gates or redirect the work while it is in flight.
What improves
Verified changetests, logs, screenshots, review
Better memoryapproved facts and decisions
Stronger systemskills, agents, and gates
Learning loopValidated evidence and reviewer decisions flow back into the context graph, so the next run starts with better knowledge.
Prinevo agent workspace agents working in parallel
Always on

Keep work moving 24/7.

Agents continue long-running delivery work even when your team is offline.

Parallel execution

Run specialist work in parallel.

Agents plan, build, review, and verify different parts of a request at the same time.

Autonomous bug fixes

Fix bugs autonomously.

Turn approved Jira bug tickets into implemented, sandbox-validated, reviewer-ready changes.

What Developers Get

A team of agents in the cloud takes your task from start to finish.

They plan, code, test, review, verify, and iterate in one coordinated run. Guide key decisions, approve important gates, and see the evidence before anything ships.

01 Specialist agents

Work with a team of agents in one multiplayer AI workspace.

Your team and specialist agents share context, collaborate on decisions, and coordinate product, architecture, implementation, QA, and code review in one place.

02 Model and reasoning

Use the right model for each job.

Choose the model and reasoning level for every specialist agent based on the complexity, quality bar, speed, and cost of the task.

Prinevo model and reasoning panel showing GPT-5.6 with High reasoning assigned to the Architect Agent.
03 Cost and usage

Control cost across every agent run.

Set budgets and track usage and spend by feature, agent, stage, and model. Use higher-capability models where quality matters and lower-cost models for routine work.

Feature usage$14.82
62% of budget
ArchitectureGPT-5.6 · high$4.12
ImplementationClaude Sonnet$8.76
VerificationGPT-5.4 mini$1.94
04 One shared session

See the whole run in one place.

Follow the plan, stage status, decisions, blockers, approvals, artifacts, review notes, and verification evidence from request to completed change.

Prinevo work overview showing delivery stages and which stage needs attention.
05 Access and audit

Control what every agent can access.

Scope tools, environments, data, and actions by agent. Every request, approval, tool call, and change is recorded in an audit trail.

Agent access policyAudit enabled
Architect Agentarchitecture: read · decisions: write
Scoped
Implement Agentcode: write · tools: approved
Allowed
Deploy Agentproduction release requested
Approval required
06 Control when it matters

Guide work at the right gates.

Approve, nudge, retry, or redirect meaningful work before it drifts. Low-risk changes can continue autonomously when the evidence is strong.

Prinevo architecture approval gate recommending separate repositories, with options to use a monorepo, give feedback, or approve the design.
07 Sandbox verification

Know the change works before it ships.

Agents run the change in a sandbox and return the proof: test results, logs, screenshots, contract checks, and review-ready evidence tied to the original request.

Verification workspace showing completed tests, contract checks, captured screenshots, rollout notes, and a ready-for-review status.
08 Work from Slack

Start work where your team already works.

Ask Prinevo to review a pull request or start a delivery workflow from Slack without losing the governance and context behind the work.

Slack request asking Prinevo to review a Bitbucket pull request.
09 Central context

Give every agent the same engineering context.

Agents start each stage with the latest architecture, decisions, contracts, ownership, and validated evidence instead of starting from scratch.

Prinevo memory benchmark
82.60%LongMemEval 83.89%LoCoMo
Read the benchmark paper
10 Self-improving

The software factory improves with every run.

Learning stores approved facts, decisions, failed checks, rollout results, and reusable fixes so every new run starts with better context. Evolution uses run traces to grow the skills and specialist agent team your company needs.

01 Learning

Completed work, reviewer decisions, failed checks, rollout results, and reusable fixes are stored as approved organizational memory so the next run starts with better context and avoids repeating mistakes.

01 CaptureFacts and decisions
02 ApproveOrganizational memory
03 ImproveBetter next run
02 Evolution

Run traces reveal where the system needs new skills, stronger checks, new workflows, or new specialist agents. The agent team grows with your company and the work it needs to deliver.

Recommended next additions
Specialist agent for repeated workAgent suggested
Reusable skill from successful tracesSkill suggested
Review gate where failures clusterGate suggested
Configurable expert gates

Reduce AI slop and rework from bad agent direction.

Small changes can move fast. For meaningful changes, Prinevo lets experts review PRD, HLD, LLD, implementation, and verification gates before work drifts downstream. Teams can auto-approve low-risk gates and require accountable owners only when the change needs judgment.

PRD gate HLD and LLD review Owner approval Verification gate
Sandbox-validated changes

Know the change works before it ships.

Prinevo runs changes in a sandbox and brings back the proof your reviewers need: tests, logs, screenshots, contract checks, rollout notes, and review context.

01

Run the change in context.

Agents bring up the right services, seed the right data, and validate the behavior against the original request.

02

Attach evidence to the work.

Test output, logs, screenshots, contracts, and rollout notes travel with the change instead of living in scattered tools.

03

Use gates only where they matter.

Small tasks can move autonomously. Larger changes can require PRD, HLD, LLD, implementation, or verification approval.

Controlled Delivery Flywheel

A control plane for your software delivery flywheel.

Prinevo connects context, coordination, governance, verification, and learning so faster shipping creates faster feedback, better decisions, and stronger future runs.

01 Context

Right context before work starts.

Agents use architecture, decisions, contracts, owners, prior evidence, and repo knowledge before they plan or change code.

02 Coordination

Coordinated delivery across teams.

Specialist agents and people line up work across teams, repos, services, tests, reviews, and rollout instead of working in isolated sessions.

03 Governance

Pick the right model and control cost.

Select approved models per task, scope tools and access, set budgets, enforce cost policies, and choose which approvals are required.

04 Verification

Know the change works before shipping.

Changes are tested in sandboxes with logs, screenshots, contracts, rollout notes, and evidence attached to the work.

05 Learning

Every run makes the next one stronger.

Reviewer decisions and verified evidence update shared context so future agents start with better knowledge and stronger checks.

06 Evolution

The agent team grows with your needs.

Reusable skills and specialist agents are added as your company, systems, and delivery workflows evolve.

Technical Architecture

For teams that need control at scale.

Open the technical architecture page to see how Prinevo connects shared context, specialist agents, governance, sandbox verification, and learning into one delivery system.

View technical architecture
Platform

Give every agent the context, controls, and verification 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

Published headline scores across two long-context memory benchmarks.

Paper
LongMemEval82.60%
LoCoMo83.89%
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.
  • Shared skill libraryReusable agent skills are managed once and shared across every user, team, and workflow so the whole software factory improves together.
  • Long-running workKeep multi-step tasks moving through review, verification, and rollout.
  • Human steeringApprove key gates at each stage and step in when direction changes.
03 Verification 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

Give agents more autonomy without losing control.

Choose models, grant stage-specific tools and access, manage usage and cost, enforce policies, and keep a clear audit trail of every agent action, approval, decision, and proof package.

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.
  • Audit trailTrack what each agent did, which tools it used, what evidence it produced, and who approved key gates.
05 Learning & Evolution foundation

Learning & Evolution

Every completed run updates your engineering brain. CodeGraph and memory stay current so agents work from the latest repo and organizational context. Run traces reveal missing skills, weak workflows, and new specialist agents the system should add next. As patterns repeat, Prinevo turns them into reusable skills and specialist agents so more of the delivery loop can run autonomously.

What your team gets
  • CodeGraph stays currentRepository maps update after changes so agents start from the latest code and architecture context.
  • Agents evolveTraces show where agents need new skills, checks, and workflows, then reviewed improvements are promoted.
  • The agent team grows with youPrinevo learns which specialist agents are needed and adds them to get the next job done.
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.
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
Rework
Can generate more code quickly, but weak PRDs, HLDs, LLDs, and missing ownership push issues downstream.
Reduces Rework with configurable gates and accountable owners who can steer agents before code drifts.
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.
Model, Cost, and Access
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 from one place.
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.
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
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.

Tell us about the software delivery work you want your team of agents to handle. Your request will go directly to the Prinevo team.