Resources for Factory

Compare Droid vs Devin for interactive coding, delegated multi-step work, and review handoffs. Separate saved instructions, persistent compute, and model configuration.

Compare Factory vs Devin on where code runs, which services receive model requests, and what sessions and telemetry retain. Understand the limits of private deployment.

Compare Factory vs Cognition on administering Droid and Devin: user access, model policy, command permissions, automation ownership, and audit records.

Backup recovery testing for coding agent changes verifies restore procedures, integrity, dependencies, and recovery objectives with reviewable evidence.

Content Security Policy testing for coding agent changes verifies directives, nonces, report-only rollout, and browser behavior without weakening protection.

CORS testing for agent-generated services verifies allowed origins, preflight handling, credentials, caching, and failure behavior in browsers.

Compare OpenAI dots with Factory on model selection, pricing and usage limits, cloud computers, projects, background work, and permissions.

Compare OpenAI dots with Factory on software delivery, cloud computers, background work, model selection, pricing, and action approvals.

Luke Alvoeiro explains the multi-agent architecture behind Factory Missions. Explore planning, independent validation, structured handoffs, and selective parallelism.

Matan Grinberg discusses recursive self-improvement and AI adoption with Sabrina Halper. Separate model improvement, engineering feedback, token spending, and useful outcomes.

Session cookie testing for coding agent changes verifies scope, transport protections, expiration, rotation, and logout behavior across application routes.

Tenant isolation testing for coding agent changes verifies authorization, storage boundaries, cache keys, and background jobs before shared services ship.

What are OpenAI dots? Explore their cloud computers, ongoing work, models, and approvals alongside Factory, a model-agnostic alternative for software development.

API rate limit testing for coding agent changes verifies quotas, 429 responses, retry guidance, and recovery before a service reaches production.

File upload security testing with coding agents verifies type, size, storage, authorization, and retrieval controls with reproducible fixtures.

Memory leak testing for agent-generated code measures retained memory across repeated workloads and gives reviewers reproducible evidence.

SSRF testing for coding agent changes verifies URL validation, network boundaries, redirects, and DNS behavior before server-side requests ship.

Telemetry schema checks for coding agent changes keep metric, span, and log attributes stable, bounded, and safe for downstream systems.

Authorization tests for coding agent changes require denied-path coverage, object-level cases, policy fixtures, and evidence at the enforcement point.

Concurrency testing for coding agent changes combines race detectors, controlled execution orders, invariants, and stress runs with reproducible evidence.

Queue consumer safety in agent-generated code depends on idempotent effects, correct acknowledgments, bounded retries, and tests for duplicate delivery.

Time zone testing for agent-generated code covers daylight-saving transitions, database updates, ambiguous local times, and deterministic clocks.

Webhook verification for coding agent changes requires raw-body signature checks, replay defenses, secret rotation, and negative tests before merge.

Database query plan review for coding agent changes compares representative plans, measured execution, and schema context before a performance-sensitive merge.

Kubernetes manifest validation for coding agent changes should use the target API server, show field ownership, and keep rollout decisions with reviewers.

Log redaction review for coding agent changes starts with a data inventory, tests structured output, and verifies that useful diagnostics survive.

Protocol Buffer compatibility review for coding agent changes checks field history, generated clients, mixed-version behavior, and rollback safety.

Retry safety review for agent-generated code verifies idempotency, retry ownership, backoff, time limits, and evidence from failure-path tests.

API client generation with coding agents should start from a reviewed contract, use a pinned generator, and validate compatibility before updating consumers.

Build provenance connects coding agent artifacts to source, workflow, and inputs. Generate attestations in CI and verify them before release.

Code ownership routes coding agent pull requests to people who understand the affected systems. Keep ownership rules current, specific, and protected.

Container image hardening with coding agents works best when the task fixes a measured image risk, preserves reproducibility, and produces evidence for review.

Test data privacy for coding agents requires approved fixtures, restricted access, and checks that prevent sensitive records from entering prompts, logs, or commits.

Accessibility testing with coding agents combines deterministic scans, keyboard flows, semantic inspection, and human review. Build useful evidence into every UI change.

Agent readiness exposes avoidable setup, search, and validation work. Connect repository improvements to token efficiency without inventing a savings benchmark.

Use agent readiness to find the repositories blocking an AI rollout, prioritize fixes, and measure progress with Factory’s reports and dashboard.

Write an AGENTS.md file with verified commands, clear scope boundaries, and useful completion checks. Test the instructions on a small task before expanding them.

AI agent controls for private networks need enforceable permissions, sandboxing, managed model policy, and review gates. Build a testable policy for Droid.

API compatibility checks help coding agents change services without surprising consumers. Compare contracts, trace callers, and validate old and new behavior.

Model routing can lose its price advantage when a switch discards cached context. Factory’s research explains why task state belongs in the decision.

Configuration drift repair with coding agents starts from approved baselines, separates evidence from fixes, and keeps changes reviewable.

Context compaction should preserve decisions and file state, not just shrink a transcript. Factory’s published study shows what to test after a long coding session.

A coding agent audit trail connects identity, intent, tool activity, validation, review, and deployment. Learn what evidence to retain without collecting unnecessary data.

Database migrations with coding agents need staged schema changes, compatibility tests, rollback evidence, and human approval without hiding operational risk.

AI dependency updates need more than a version bump. Scope compatibility work, inspect transitive changes, run focused tests, and keep every update reviewable.

Feature flag cleanup requires proof that a flag is stale. Coding agents can remove the obsolete branch and evaluation call while testing the permanent behavior.

Localization testing with coding agents checks translated interfaces for layout, formatting, direction, missing messages, and functional regressions.

Coding agent observability work needs stable signal names, bounded cardinality, and behavioral evidence. Learn how to review instrumentation changes.

A coding agent task specification needs a bounded outcome, repository context, acceptance criteria, and verification steps. Use this practical structure for delegated work.

Coding agent productivity metrics should connect cost and usage to delivery, quality, and business priorities. Measure outcomes without rewarding code volume.

Runtime upgrades with coding agents can turn compatibility discovery, dependency updates, and validation into a reviewable migration.

Visual regression testing with coding agents combines stable screenshots, targeted interaction, and human review of intentional UI changes.

AI coding agents in monorepos need explicit package boundaries, local instructions, dependency graphs, and affected tests. Make large repositories easier to change safely.

Test fixture maintenance with coding agents can remove hidden dependencies, reduce setup cost, and preserve the behavior each test proves.

Cross-repository coding agents need explicit dependency order, isolated workspaces, compatible changes, and release evidence. Use this plan for coordinated updates.

Published Factory case studies show enterprise software work across modernization, secure packages, financial products, search infrastructure, and day-0 launches.

EU AI deployment requires separate decisions about session content, inference, identity, and control-plane ownership. Compare regional Factory hosting with Factory Private.

Evaluate coding agents with representative tasks, reproducible environments, independent acceptance checks, and a record of cost and human intervention.

Enterprise software modernization needs clear scope and reviewable evidence. Comarch’s published migrations show how Factory Missions fit that work.

Evaluating self-improving software requires stable checks and comparable work. Connect workflow changes to accepted outcomes, human correction, cost, and later failures.

Compare Factory and Claude Code token efficiency using accepted changes, billed usage, cache behavior, and reruns. Keep model and harness effects separate.

Compare Factory Managed vs Private across control-plane ownership, model access, telemetry, and operations. Choose a deployment around your actual requirements.

Factory Private architecture puts the control plane in your infrastructure. Plan the model, network, telemetry, and operating boundaries before a pilot.

Compare Factory Droid and Cursor through enterprise deployment, privacy, administrative controls, editor integration, and a practical pilot.

Compare Factory vs Warp across cloud orchestration, harness choice, context, and deployment. Follow current official sources and inspect a real delivery workflow.

Evaluate fast inference for coding agents across providers and gateways. Compare response latency, model compatibility, cost, and time to a validated result.

Flaky test triage needs repeatable evidence, controlled reruns, and a narrow fix. Learn how coding agents can investigate intermittent CI failures without hiding them.

Infrastructure as code changes need plan evidence, policy checks, and controlled application. Learn how coding agents can prepare reviewable cloud changes.

Least-privilege access for coding agents limits files, commands, networks, and credentials to what each task needs. Use this review checklist before granting autonomy.

Plan legacy modernization with private AI around behavior preservation, internal dependencies, and reviewable milestones. Keep code and validation inside the approved boundary.

Long-running coding agents need bounded milestones, externalized state, independent validation, and clear stop conditions. Use these controls for multi-day work.

Private AI keeps model research close to protected code, weights, and evaluation data. NVIDIA’s published Factory deployment shows why task trajectories matter.

Private AI package maintenance depends on repeatable builds and durable context. Chainguard’s published experience shows why long-running sessions deserve evaluation.

Enterprise AI depends on engineering context that security teams can approve. Nav’s published results connect reduced context switching with controlled access.

Release note automation needs verified scope, audience-aware language, and links to evidence. Learn how coding agents can draft notes without inventing impact.

Reproducible environments for coding agents make failures diagnosable and results comparable. Pin tools, preserve commands, isolate changes, and capture evidence.

Review agent-generated pull requests with bounded diffs, automated checks, ownership rules, and evidence that lets human reviewers focus on consequential decisions.

SBOM automation should track resolved components, provenance, and release artifacts. Learn how coding agents can update inventories without overstating coverage.

Self-improving software architecture connects operational evidence to reviewed changes. Define what can change, where learning persists, and who controls the next release.

Self-improving software needs evidence that a change removed a recurring failure. Factory’s published Signals research shows that loop with human approval before merge.

Build a self-improving software workflow around one recurring failure. Use Factory automations, reviewable instructions, and QA evidence without granting automatic release authority.

A software factory connects incoming signals to reviewed changes and production feedback. Explore Factory’s existing architecture, automations, and validation resources.

Test impact analysis helps coding agents select fast checks without missing affected behavior. Learn how to combine dependency data with risk-based validation.

Improve token efficiency by controlling tool output, search scope, and repeated context. Use Factory’s existing measurement and harness resources to test the result.

Secret scanning for agent-generated commits needs limited inputs, commit-time checks, and a clear response path. Learn how to prevent credentials from entering Git history.

Coding agent merge conflict resolution needs a known base, semantic checks, and focused validation. Learn how to turn a conflicted branch into a reviewable change.

Coding agent backports need explicit release branches, preserved intent, and version-specific tests. Learn how to prepare reviewable fixes without widening scope.

Review application performance after coding agent changes with stable baselines, workload-specific budgets, and reproducible evidence before merge.

Dead code removal with coding agents needs reachability evidence, small patches, and behavior-focused tests. Learn how to delete unused code without hidden breakage.

AI code data residency includes model context, session records, integrations, and telemetry. Follow the complete data path before approving a coding agent.

AI agent telemetry should answer an operational question without collecting unnecessary code. Choose metrics, identity detail, and content export deliberately.

Air-gapped AI development needs internal inference, offline artifacts, and enforced network isolation. Validate the full workflow before approving a deployment.

On-prem AI development needs more than a local agent. Place the control plane, model endpoint, build tools, and telemetry inside the approved boundary.

Self-hosted models give enterprises an inference placement choice. Verify gateway routes, credentials, model policy, and task compatibility before using confidential code.

Private AI security remediation should connect a validated finding to a bounded fix, regression tests, and approval. Keep evidence and execution inside the approved boundary.

Self-hosted AI agents require clear ownership of the control plane, session data, models, and updates. Compare the responsibilities before choosing a deployment.

SOC 2 for AI coding tools requires a review of report scope, operating period, data flows, and customer controls. Evaluate evidence instead of relying on a badge.

A software factory makes software delivery repeatable and measurable. Understand its history, the agent-native feedback loop, and the engineering work needed to operate one.

GLM-5.2 is an open-weight model family available natively in Factory. This guide covers what GLM is, how to select it in the Droid CLI and app, its reasoning levels and credit multiplier, and when to reach for it on coding work.

Follow a bug report through a software factory architecture, from context and planning to a tested patch, controlled release, and production feedback.

Select Kimi K2.5 in Factory while it remains available, check its deprecated status and credit multiplier, and configure reasoning for scoped coding tasks.

A reference guide to how large enterprises (including global consulting firms, fintechs, infrastructure providers, and SaaS companies) are automating software development, with concrete examples drawn from published case studies.

MiniMax is an open-weight model family available natively in Factory. This guide covers what MiniMax is, how to select it in the Droid CLI and app, its reasoning levels and credit multiplier, and when to use it for coding work.

Compare software factory platforms by where work runs, what completes a task, and how access and release are governed. Distinguish agent workflows from the surrounding delivery system.

DeepSeek V4 is an open-weight model available natively in Factory. This guide covers what DeepSeek is, how to select DeepSeek V4 Pro in the Droid CLI and app, its reasoning levels and credit multiplier, and when to use it.

Agent readiness measures how well a codebase supports autonomous coding agents. This guide covers how to measure it with Factory, using the readiness report in the Droid CLI, the readiness dashboard, and the readiness reports API.