How Twindem keeps AI delivery under control.

AI coding tools are useful, but they usually operate inside a developer's terminal, chat session, or local context. That is fine for individual productivity, but weak for company delivery.

A company needs more than "the agent generated code." It needs to know what the task was, what phase it was in, who owned it, which agent worked on it, which reviewer challenged it, what findings were opened, what evidence was produced, and who approved the next step.

Twindem does not replace Codex or Claude Code. It gives the company a controlled delivery layer around them: board items, phases, review runs, evidence, usage records, and audit events.

It means AI work does not happen as an invisible side conversation. Work starts from a delivery item and moves through a defined workflow: request, refinement, implementation, review, UAT, and production.

At each step, Twindem keeps useful context attached to the work item: agent runs, summaries, review findings, evidence artifacts, token usage, cost, comments, handoffs, and board events. The company can decide where automation helps and where a human gate is required.

If one agent produces the work, the same agent should not be the only judge of whether that work is correct. Twindem's model separates implementation and review.

One agent can plan or implement. A second agent can challenge the output, inspect risks, open structured findings, and drive a fix loop before the work moves forward. This does not remove human responsibility; it gives the human reviewer a stronger, more consistent starting point.

Yes. This is one of the strongest parts of the workflow. Architecture tasks can be used to explore a direction, compare options, document tradeoffs, and then propose the development tasks that should follow.

That means an architecture decision does not stay disconnected from delivery. Twindem can keep the reasoning, proposed implementation steps, risks, dependencies, and evidence expectations attached to the board, so the team can review and approve the next development work intentionally.

For companies, this creates a cleaner bridge between technical planning and execution: AI can help decompose architecture into actionable work, while humans still decide which tasks enter the delivery workflow.

No. Twindem is built around human control, not blind automation. The second agent helps catch problems and structure the review, but the company still controls gates such as accepting a refinement plan, moving work to UAT, approving release evidence, and deciding when something is production-ready.

A normal board usually tracks status. Twindem tracks the AI delivery process behind the status. A work item can carry its phase, type, priority, owner, assignee, claims, comments, handoffs, execution runs, review findings, evidence, usage, cost, and audit events.

That makes the board more than a planning surface. It becomes the source of truth for AI-assisted delivery.

A run can record the execution client, run role, provider, model label, model ID, device, status, outcome, timing, summary, and error summary. Usage can be attached to that run: input tokens, output tokens, cached input tokens, total tokens, cost, and whether the value is estimated.

This lets teams connect AI activity to real delivery work, instead of only seeing a monthly provider invoice disconnected from projects and tasks.

Review findings are structured records, not loose chat messages. A finding can have a severity such as blocker, critical, high, medium, low, or info. It can include a title, body, file path, line range, status, resolution note, and links to the related run and work item.

Findings can move through states such as open, fixed, resolved, or dismissed, so the team can see whether the review loop actually closed.

Evidence can include test output, logs, diffs, screenshots, command output, links, files, audit receipts, and run receipts. Evidence can be connected to a work item, a specific agent run, or a review finding.

Evidence also has a health state: unverified, healthy, warning, or failed. That helps the team separate "the agent says it is done" from "there is supporting proof attached."

Agencies often need to explain how work was delivered, especially when AI is involved. Twindem keeps the request, implementation runs, second-agent review findings, evidence, approvals, and release state attached to the story.

That makes it easier to show progress, defend decisions, and prove that AI work did not bypass the team's delivery process.

Twindem Community is the open-source local product for AI delivery workflows. It focuses on local desktop orchestration, the board-driven workflow, dual-agent loop, and local evidence.

Twindem Team is the commercial governance layer for companies. It adds members, seats, projects, admin settings, board access, company agent configuration, vault, usage visibility, audit, and commercial licensing.

Twindem is positioned as local-first where code matters. Repo work and agent sessions are designed to stay local, while Team focuses on governance metadata and delivery control: licensing, membership, board access, policy, metrics, and shared auditability.

That distinction matters: the company gets control over the process without turning every repository interaction into a mandatory cloud upload.

The admin control plane supports company agent configuration and provider keys. Provider keys are handled as protected server-side material, and admin APIs return metadata rather than exposing raw key values.

For shared AI secrets, the vault model stores opaque ciphertext and per-user wrapped keys. Wrapping happens client-side, while the server orchestrates custody, re-wrap operations, and audit records.