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Tandem

Tandem enforces policy between AI agents and the tools, data, memory, and actions they use.

Agents can reason, draft, and propose work. Tandem decides what they are authorized to see, which tools they can call, which actions must pause for approval, what memory/context they can access, and what evidence gets recorded.

This makes Tandem useful when agents touch real company systems: files, repositories, email, MCP tools, customer data, internal docs, production workflows, and long-running automations.

For platform and security teams, Tandem acts as a runtime control plane for agentic systems: scoped tool access, approval gates, permissioned memory, tenant/resource boundaries, and audit evidence.

The model proposes. Tandem enforces.

In governance terms, Tandem manages delegated authority for AI agents at runtime.

What Tandem Does

• Runs AI workflows with durable state instead of transcript-only execution.
• Scopes which built-in tools and MCP connectors are visible at each workflow step.
• Blocks tool calls that fall outside runtime policy before execution.
• Pauses consequential actions for human approval.
• Controls which company memory and context a run can retrieve.
• Records artifacts, tool events, approval decisions, and audit evidence outside the model context window.

Simple Example

An agent may be allowed to draft a customer email, but not send it.

Tandem can expose the draft tool, hide or block the send tool, pause at an approval gate, resume only after a human approves, and record the decision in the audit trail.

What Tandem Is Not

Tandem calls this runtime authority: authorization, execution control, approval, scoped memory, and audit enforced outside the model. Entrypoints such as the desktop app, TUI, web control panel, channels, and SDKs are clients of the same engine runtime.

• Runtime-owned controls: Runs, sessions, memory, context, provider secrets, MCP tools, approvals, artifacts, and audit records live outside the model.
• Governed tool execution: Built-in tools and MCP connectors can be scoped per workflow step, with approval gates for consequential actions.
• Tenant-aware runtime: Hosted and enterprise modes carry tenant/principal context through sessions, runs, context runs, memory, provider credentials, MCP secrets, and events.
• Deployable where the data lives: Tandem can run locally, headlessly, hosted, or inside customer infrastructure.
• Provider agnostic: Use OpenRouter, Anthropic, OpenAI, OpenCode Zen, or local Ollama endpoints.

Agent intent -> Runtime policy -> Scoped tool/data access -> Approval gates -> Artifacts -> Audit trail

-> AI runtime infrastructure | Enterprise readiness | Runtime trust boundaries | EU AI Act readiness | Compliance starter pack | Connect an agent via MCP

Why Tandem Exists

Agents are becoming workers. They read company context, call tools, open pull requests, draft customer communication, operate project boards, and prepare decisions that used to stay inside human-only systems.

Prompts are not permissions. A system prompt can ask a model to avoid a tool, skip a folder, or wait for approval, but the model should not be the security boundary. Tandem puts those controls in the runtime, so a workflow can grant the agent only the tools, memory, and actions needed for the current step — and deny anything outside that scope.

Companies also need central AI context without flat access. A permissioned company memory should know what the company knows, but an agent acting for one team, tenant, project, or user should only retrieve the slice it is allowed to use.

What Tandem Governs

• Company knowledge and memory: Runtime-owned memory, knowledge spaces, and retrieval paths designed around tenant and workspace boundaries.
• Tool and MCP visibility: Step-scoped built-in tools and MCP connector tools, with broader pre-invocation masking planned for enterprise deployments.
• Workflow execution: Durable automation and context-run state instead of fragile transcript-only execution.
• Human approvals: Runtime gates pause runs, collect approve/rework/cancel decisions, and leave evidence.
• Tenant and workspace boundaries: Tenant-aware sessions, runs, context runs, events, provider credentials, MCP secrets, memory, and contract vocabulary for resource scopes and grants.
• Connector credentials and secrets: Provider and MCP secret references are runtime-owned; connector source binding gives scoped ingestion a shared contract as that layer matures.
• Artifacts and audit trails: Outputs, validations, tool ledger events, approval decisions, and protected audit records survive outside the model context window.

Core Use Cases

Why Platform And Security Teams Care

Tandem is designed for teams that need to run AI work under real operational controls:

• Runtime authority, not prompt authority: The model can request context or a tool call; the runtime decides what is visible and executable.
• Tenant-aware records: Sessions, automation runs, context runs, event streams, provider credentials, MCP secrets, and memory paths carry tenant context in hosted/shared modes.
• Resource and grant model: Tandem models resources, principals, grants, data classes, and data boundaries so access decisions can be enforced by the runtime.
• Permissioned memory: Memory and knowledge paths carry tenant boundaries so company knowledge can become useful without becoming globally flat.
• Deployable runtime: The same runtime can run on a laptop, as a headless engine, hosted, or inside customer infrastructure as the enterprise layer matures.
• Auditability: Approval decisions, policy denials, provider secret changes, MCP activity, tool ledger events, artifacts, and protected audit records can be inspected outside chat transcripts.

Deployment Model