AgentGate vs Granite Guardian

AgentGate

An agent or its host can send each tool call to one REST endpoint, or wrap an MCP server, and get allow, deny or ask from a local classifier and a YAML policy, with an audit log and approval queue. The project is three days old, auth is off by default, and no detection accuracy is published.

Granite Guardian

One ungated Apache 2.0 model judges harm, jailbreaks, RAG groundedness, function-call errors and custom criteria, with signed weights and published evaluation code. It is trained and tested on English only, each call checks one criterion, the 4.1 prompt format differs from 3.x, and IBM's watsonx.ai lists only the deprecated 3.0 model.

AgentGate

1. Create a session with the user's real goal first. The goal-alignment head scores every later call against it
2. Pass earlier tool results in history_override with untrusted: true for web or email content, or the injection head sees only the action
3. On ask, stop and tell the user the decision_id. Polling /v1/decisions/:id needs the dashboard key
4. Treat allow_with_checkpoint as allow and keep the checkpoint_id for an undo
5. Expect HTTP 404 with not_found for an unknown session and 401 when the owner has set an agent key

Granite Guardian

1. Append the <guardian> block as the final user message, with the mode line, ### Criteria: and ### Scoring Schema:. Copy the strings from the model card, because no package builds them
2. Use the no-think instruction for gating and parse <score>. Think mode writes a reasoning trace first, and the card's examples allow up to 2,048 output tokens
3. Treat yes as the criterion being met, which for built-in criteria means the risk is present. Treat a missing <score> tag as a failed check
4. Pass retrieved text through documents= and tool schemas through available_tools= in apply_chat_template, not inside the message text
5. Under Ollama, set num_ctx in the request options. IBM's docs say the default context is short and long requests are truncated