@memberjunction/ai-agent-harness

Run an external agent harness — Claude Code, Codex CLI, OpenCode, Gemini CLI, Pi — as the reasoning substrate for a MemberJunction agent, while MJ keeps identity, permissions, governed data access, payload contracts, HITL, cost control and run-level audit.

Design principle: the harness is a substrate, not a peer. MJ owns the run record, the credentials, the tool surface and the approval flow. The harness owns the reasoning inside a turn.

Design plan: plans/external-agent-harness.md

The core idea: a harness turn is a Loop iteration

BaseAgent already runs an iterate → decide → execute-steps → iterate loop where the "decide" input is a prompt execution. This package substitutes a harness turn for that prompt call and changes nothing else.

   ┌─ BaseAgent loop (unchanged) ──────────────────────────────────┐
   │                                                               │
   │   executePrompt() ──► [ HarnessAgentBase override ]           │
   │        │                      │                               │
   │        │                      ├─ adapter.RunTurn(input)       │
   │        │                      ├─ accumulate usage             │
   │        │                      └─ write AIPromptRun            │
   │        ▼                                                      │
   │   DetermineNextStep  (inherited from LoopAgentType)           │
   │        ▼                                                      │
   │   validate ─► execute actions / sub-agents / skills           │
   │        ▼                                                      │
   │   checkExecutionGuardrails ─► next turn                       │
   └───────────────────────────────────────────────────────────────┘
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The harness ends each turn by emitting the Loop next-step JSON envelope. MJ then executes any actions, sub-agents or skills through its own validated machinery and resumes the session with the results.

Why this matters: every guardrail, payload ACL, HITL gate and accounting path already written for Loop agents applies to harness agents with no new enforcement code — and there is exactly one authority channel to audit, not two.

Two registrations, one metadata column

ClassFactory registrations are namespaced per base class, so 'HarnessAgentType' is registered twice against different roots:

Both read AIAgentType.DriverClass, so one metadata value selects both halves. This is the mechanism working as designed — AgentRunner already treats the type's DriverClass as a BaseAgent key and falls back to plain BaseAgent when unregistered, which is why every Loop agent gets the base execution class today.

Adapters

Register your own with @RegisterClass(BaseHarnessAdapter, 'MyAdapter') and point a harness row's DriverClass at it. No core changes required.

Capability honesty

AIAgentHarness.CapabilitySettings (typed as IHarnessCapabilitySettings) declares what an adapter actually implements, because the runtime emulates what is missing. Gemini CLI reports SessionResume: false, so context is replayed each turn and those extra tokens are budgeted against the run's guardrails rather than quietly absorbed.

Claiming a capability that is not wired up produces a silent behavioural gap, not an error. Report false and let the runtime compensate.

Sandboxes: the provider owns process placement

Adapters never call spawn. They run everything through SandboxExecutor, obtained from the handle the provider returns.

⚠️ The local provider scopes a directory; it does not contain the process. networkPolicy is advisory there. Anyone who believes 'none' is enforced locally has a false sense of containment, which is worse than knowing the boundary is soft.

HarnessProcess is deliberately stream-based, not ChildProcess-based — a Kubernetes exec is streams over a websocket and a remote runner is HTTP, and neither could honestly implement a ChildProcess contract. It also makes adapters unit-testable with a fake executor: no binary, no container, no network.

WorkspacePath means "as the harness sees it"

A host path under the local provider; a container-internal path under Docker. Pass it to harness processes — do not open it with fs unless you know you are on the local provider.

Configuration

Per-agent, in AIAgent.TypeConfiguration, validated against AIAgentType.ConfigSchema:

{
  "harnessName": "Claude Code",          // lookup into MJ: AI Agent Harnesses
  "sandbox": {
    "provider": "local",                 // local | docker
    "image": "ghcr.io/memberjunction/harness-sandbox:latest",
    "workspaceScope": "agent-user",      // run | agent | agent-user
    "networkPolicy": "mcp-only"
  }
}
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Workspace scope decides how long files live: run is discarded, agent is shared across every run of that agent, agent-user (default) is per agent per user — continuity without one user's working files leaking into another's session.

Accounting — why every turn writes an AIPromptRun

Run totals are derived: calculateTokenStats sums AIAgentRunStep.PromptRun rollups. A turn that records no prompt run contributes nothing, so the run reports zero tokens and zero cost forever — and its cost ceiling has nothing to compare against.

AIPromptRun.PromptID, .ModelID and .VendorID are all NOT NULL, and each resolves to a real catalog row rather than a placeholder:

If none resolves, the runtime fails loudly rather than skipping the row. A silent skip is exactly how a cost ceiling stops protecting anything.

Credentials

MJ: AI Agent Credentials records the grant edge — which credentials an agent carries into its sandbox. Custody stays in MJ: Credentials / CredentialEngine.

Environment injection is the only channel by which a secret reaches the harness, and it carries exactly what was granted — never the MJAPI process environment, never DB credentials, never a user token.

Distinct from MJ: AI Credential Bindings, which is inference-selection plumbing for AIPromptRunner failover when MJ itself executes a prompt.

The audit boundary

MJ records what crosses the boundary: MCP loopback reads and the turn-end step. Activity inside the sandbox — file edits, shell commands — streams to onProgress for live view but is not persisted as run steps.

This "opaque super-step" granularity is intentional. In-sandbox behaviour is governed by posture policy, not by run steps. Widening it is a design change, not a bug fix.

Deployment

Do not bake harness binaries into the MJAPI image. A harness running inside the API container inherits that container's network reach and IAM role — the wrong blast radius for a process executing an autonomous agent's shell commands.

Known gaps

• PermissionHooks: false on every adapter. The strict posture needs an MCP permission-prompt tool that does not exist yet. Reported honestly so the runtime cannot assume interception it lacks.
• networkPolicy mcp-only / allowlist are not enforced at the packet level under Docker — documented as such rather than aliased to open.
• MCP loopback is not yet wired. HarnessSessionConfig carries the fields; the server and per-run scoped credential are still to come.
• ModelID uses the declared model, not the model the harness reported for the turn. The refinement belongs in resolveAccountingIds once adapters surface it.

License

Business Source License 1.1 — see LICENSE for details.