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Agents

An agent is an LLM-backed assistant you build, publish, and embed against your own governed data. Honeyframe agents come in two shapes:

  • Simple agents — a system prompt, a model, an optional set of tools, and a knowledge base. The model decides when to call a tool and composes the answer.
  • Structured visual agents — a visual flow of typed blocks (routing, LLM, tool call, Python, output, …) that you lay out explicitly, for deterministic, inspectable control over how the agent responds.

Both run through the same governed runtime: every SQL stays server-side, PII masking applies on every path, per-agent guardrails screen inputs and outputs, and every call is traced and costed.

Where agents live​

SurfaceWhat it does
Agent BuilderAuthor a simple or structured agent — prompt, model, tools, knowledge, guardrails.
Agent HubBrowse and manage published agents across the project.
Agent ChatTalk to a published agent directly.
Agent ToolsDefine the tools an agent may call.
Knowledge BanksVector stores an agent retrieves from (RAG).
Agent ReviewScore an agent against a test suite of questions with judge traits (honesty, grounding, refusal-correctness).

Guardrails​

Every agent can carry a guardrail pipeline that runs on the query (input) and the response (output). Built-in guardrails include PII detection/redaction, numeric grounding (verify every figure against the query result), period-completeness caveats, and table-completeness disclosure. Guardrails degrade open — a guardrail failure never blocks a correct answer.

Tracing & cost​

Each agent turn produces a structured trace (which blocks fired, which tools ran, latencies) and a token/cost ledger. Spend is visible per model in Administration → Monitoring.

See also​