The market for agent-powered software delivery is fragmenting fast. In the last twelve months, platforms have emerged around different parts of the software lifecycle: delivery systems, native code-host controls, workflow automation, application builders and review. This comparison asks a narrower question than "which tool is best": where does delivery governance live, what evidence survives, and how can the products compose?

The landscape

Factory.ai ("Droids")

Factory is the closest comparison here on delivery outcome. Its public materials describe "ticket to mergeable PR" automation across feature development, migrations, code review and testing, with specialized agents ("droids") and governance controls around models, tools, MCP servers, autonomy levels and telemetry. Factory also describes enterprise sovereignty and on-premise options. In the sources reviewed for this comparison, we found no public documentation for an inspectable source distribution, portable agent definitions, offline-verifiable cross-tool evidence or a community eval library. Best for large enterprises that want a managed software factory and the budget for enterprise licensing.

Tembo (Agent Studio)

Tembo pivoted from managed Postgres to agent orchestration in 2025. Its Agent Studio (launched May 2026) is an MIT-licensed, self-hosted control plane where agent definitions live in a Git repository as the source of truth, so every change to an agent is a PR-reviewable artifact. It orchestrates Claude Code, Codex, and Cursor's background agents without shipping its own coding agent, and it is agent- and model-agnostic with a model gateway that fails over across providers. Runs, audit logs, and identity stay inside your environment. Since version 2026.9.1 (September 2026) it also offers optional per-agent eval suites - colocated assertion files that must pass before an agent draft can be promoted - so agent quality is now gated, though there is still no documented cross-run evidence record or progressive-autonomy model (see tembo.io). Best for teams who want agent-agnostic orchestration with Git-native config and are comfortable managing their own infrastructure.

Dify

Dify is the most established horizontal AI application platform: visual workflow building, RAG pipelines, model provider integration, and a large plugin ecosystem, with a self-hosted community edition. It is not SDLC-specific but can be used to build SDLC workflows. Governance is basic RBAC and audit in the Enterprise tier, model support is broad, and its audit is general with no SDLC-specific evidence or eval library. Best for teams building general AI applications who want a self-hosted visual workflow builder.

GitHub / Copilot (native agent governance)

GitHub's native agent control plane (GA February 2026) adds centralized policy management and audit for Copilot and agent activity org-wide, only inside GitHub's ecosystem. It has since grown three more native surfaces. Agentic Workflows (public preview 11 June 2026) make agent workflows programmable in Markdown and run them through GitHub Actions, behind a sandbox, an Agent Workflow Firewall, and a safe-outputs gate. Copilot code review became callable over the REST API on 2 October 2026, and local sandboxing for Copilot sessions went generally available on 7 October 2026, bounding filesystem, network, and credential access to developer- or organization-defined policy. GitHub now contains and orchestrates agents well - it still does not govern agents in GitLab, Linear, or other tools, and it has no cross-tool audit trail. For a deeper look at governance beyond GitHub's native controls, see the use case breakdown, or the full Farna Control vs GitHub comparison. Best for GitHub-only shops who want orchestration, containment, and basic governance of Copilot activity.

GitLab Duo / the governed software factory

GitLab announced the foundation for a governed software factory on 6 October 2026: goal-driven agentic workflows across the lifecycle, GitLab Artifact Central, the GitLab Dependency Firewall, per-job scoped secrets in the Secrets Manager, Duo Agent Platform Impact Analytics (cost and impact by team, task, and model), and a GitLab Security Standard that moves a metric from detected to verified remediation. Custom flows run under GitLab's shared identity and policy, and GitLab describes the whole thing as creating an "evidence chain" - a record generated by GitLab, of what GitLab saw. It is ecosystem-scoped and forge-generated. GitLab reports that active users of its agentic software development grew 200% year over year over the last three months, a usage claim, not a revenue one. Self-managed deployment is available but sales-gated. Best for GitLab-only shops who want AI agents integrated into their existing DevSecOps platform, under GitLab's own controls.

Cursor / Windsurf

IDE-centric AI coding agents. Cursor (acquired by SpaceX in August 2026) and Windsurf focus on AI-assisted coding in the editor, not full SDLC orchestration or governance, with minimal governance, no self-hosting, IDE-specific platform support, and no cross-tool audit trail. Best for individual developers who want AI-assisted coding in their IDE.

Cycode (Agentic Development Security Platform)

Cycode launched its ADSP in March 2026, targeting security and compliance teams. It provides AI Bill of Materials (AIBOM), MCP enforcement, policy controls mapped to SSDF/NIST/SOC2/ISO27001, and shadow-AI discovery. Governance is security-first, for a different buyer (CISO) than SDLC orchestration platforms; self-hosting is not specified, and audit is compliance-focused (SSDF, NIST, SOC2). Best for security teams who need visibility into AI tool usage across the organization.

Workflow tools (n8n, Temporal, LangGraph)

Generic automation and workflow-orchestration tools: n8n is visual workflow automation, Temporal is durable execution, and LangGraph is a framework for building agent workflows. None are SDLC-specific or governance-first. Their governance is general (retries, error handling), self-hosting varies (n8n and Temporal self-hosted; LangGraph a framework), and audit is general with no SDLC-specific evidence or eval library. Best for teams building custom workflow automation who want infrastructure, not a governed SDLC platform.

Qodo (code review and standards platform)

Qodo (formerly CodiumAI) reviews what coding agents and developers produce: multi-agent pull request review with full-repository context, standards mined from PR history, and an Agentic Toolbox of Claude Code, Codex and Kiro plugins plus a CLI and an MCP server that let an agent run a pre-PR review against the organization's own rules. It supports GitHub, GitLab, Bitbucket, Azure DevOps and Gerrit, and deploys as SaaS, single-tenant VPC, on-premises or fully air-gapped, with self-hosting Enterprise-only and sales-gated. It does not run or dispatch agents, by design, so it has no runtime governance, autonomy levels or per-run provenance. Best for teams whose problem is review throughput, review noise and standards drift inside existing repositories, and see Farna Control vs Qodo: review governance and delivery governance are different things for the boundary between the two.

Code review point solutions (CodeRabbit, Greptile)

They compete for the "AI reviews your PR" slice specifically: single-stage tools, not full SDLC orchestration or governance platforms, with review-stage governance, review history only, and varying self-hosting. Best for teams who want AI-powered code review without changing their broader SDLC toolchain.

Where Farna Control fits

Farna Control is the Agentic Delivery Governance platform in this comparison. It runs on Docker Compose or Fly.io, governs GitHub and GitLab, works with supported model backends including Anthropic, OpenAI, Azure OpenAI and local Ollama, dispatches any agent that meets its contract, keeps an append-only SHA-256 linked audit chain with audit export, and applies SDLC-specific controls. You set each pipeline's autonomy level and eval scores supply evidence for raising it, while token budgets, a spend circuit breaker and an org-wide pause keep that autonomy bounded. The differentiator is the delivery record across tools, not ownership of the agent runtime.

The distinction that matters most now is where the record comes from. A platform's evidence chain records what that platform saw. Farna Control records what happened - across every tool a change touches - in a chain you can verify yourself: offline-verifiable evidence, where a party holding only the export and a pre-published verification key can verify authorship, re-verify the embedded audit chain, and re-derive every derived value, with no vendor service involved.

For detailed comparisons, see Farna Control vs Factory.ai, Farna Control vs GitLab's governed software factory, Farna Control vs n8n, Farna Control vs Dify, and Farna Control vs GitHub. For a broader look at what self-hosted agent governance requires, see the self-hosted agent governance guide.

The principles behind this: self-hosted, no telemetry means your data doesn't have to leave your infrastructure, and humans in the loop where it matters means automation never gets a blank check.