Software factory FAQ
Short answers about how a software factory helps coding agents deliver production-ready changes with context, coordination, verification, and learning.
A software factory for coding agents is a control plane around AI coding tools. It connects product and engineering context, coordinates multi-repo work across product requirements, frontend, backend, workers, and infra, verifies changes in a sandbox, packages reviewer evidence, and captures learning for future runs.
A coding agent can generate or edit code. A software factory coordinates the full delivery path around that code across product context, architecture, owners, contracts, tests, rollout order, monitors, rollback path, and PR evidence.
Teams need a software factory because faster code generation does not automatically create production-ready software. The hard part is making sure the change fits the architecture, works across repositories, respects service contracts, passes the right tests, and can be trusted during rollout.
Prinevo.ai packages the product spec, architecture plan, coordinated multi-repo changes, code review evidence, test reports, logs, screenshots, contract results, rollout order, monitors, rollback path, PR summary, and lessons for the next run.
Organizational memory is the connected product and engineering context the factory uses before making a change. It includes product behavior, customer impact, workflows, repositories, owners, contracts, infrastructure, decisions, incidents, rollout history, and facts learned from previous runs.
Prinevo's context graph is a domain-pluggable long-term memory system that stores typed subject-predicate-object claims linked to resolved entities instead of only saving prose summaries or text chunks. It records when each claim was observed and when it was true, so updated facts can replace old ones without erasing history. Hybrid retrieval combines relevance, recency, and entity-based paths, helping agents answer questions that require multiple facts, temporal comparison, complete lists, or multi-hop reasoning.
Read the context graph architecture and benchmark methodology.
A context graph retrieves the relationships around a request, including product behavior, repositories, owners, contracts, decisions, incidents, and evidence from earlier runs. Agents start with grounded organizational context, reuse the same approved facts across planning, implementation, verification, and review, and spend less time reconstructing information that the team has already established.
Prinevo uses bi-temporal supersession: it records when a claim was observed and when the claim was true. When an owner, contract, decision, or system fact changes, the newer claim can become current without deleting the earlier state. Agents can therefore answer both current-state and historical questions without treating outdated information as the latest truth.
Yes. Prinevo's context graph is model-agnostic and shared across specialist agents. Product, Architect, Implement, QA, Code Review, and Learning Agents can retrieve the relevant approved context for their stage, even when different models are selected. A domain profile changes the entities and relationships that matter without rebuilding the underlying memory engine.
Prinevo combines relevance, recency, and entity-anchored retrieval, then selects an answer strategy based on whether the question needs a point lookup, complete list, comparison, aggregate, or temporal answer. If the first pass lacks evidence, retrieval can widen before the system abstains. This keeps context targeted while preserving a path to broader evidence when needed.
Prinevo reports 83.89% on LoCoMo and 82.60% on LongMemEval using two independent public long-term memory benchmarks. These results show performance across conversational and long-session retrieval tasks, but they are not presented as a controlled head-to-head ranking against other systems because published evaluations may use different judges, prompts, sample cuts, and metrics.
Review the benchmark methodology, limitations, and full results.
A software factory verifies agent-built code by running relevant tests, contract checks, integration flows, logs, screenshots, migration checks, monitor checks, and rollout validation. The output becomes evidence reviewers can inspect before merge or deployment.
Yes. Once the factory has engineering context, it can connect runtime signals to code, repositories, owners, infrastructure, deployment history, policies, and customer impact. That helps teams investigate cost spikes, incidents, security reviews, and compliance readiness.
No. A software factory gives engineers and agents a shared delivery system. Agents can do more coordinated work, while engineers guide judgment, review evidence, make release decisions, and improve the factory over time.