Use case · Agent supervision

Agents in production need supervision, not hope.

Agents are prompt injection with a budget. ShadowIQ traces every agent hop, red-teams per-step, enforces tool-use allowlists, and sandboxes untrusted memory.

See agent supervision in action Platform overview

What this is

ShadowIQ agent supervision provides per-hop tracing, evaluation, and policy enforcement for long-horizon AI agents — including tool-use allowlists, memory isolation, runtime sandboxing, and cryptographically signed decision records at every agent step.

How it fits · explainer

The before / after, in one picture.

Where it hurts

You've heard this one before.

Agents calling tools nobody explicitly authorized.
Long-horizon memory loops you can't debug after the fact.
No evaluation methodology for multi-step agent pipelines.
Third-party agents in your stack with no runtime visibility.

What we do about it

Three moves.

1
Per-hop trace.
OTel-native. Every tool call, memory read, and sub-agent invocation captured with inputs, outputs, and decisions.
2
Runtime isolation.
Untrusted tool outputs (web pages, emails, documents) flow through the gateway before re-entering the agent graph. RAG becomes safe.
3
Per-step red-team.
Score the agent per-hop and end-to-end. Detect goal drift, injection from retrieved content, and over-use of high-risk tools.

Outcomes

Numbers, not adjectives.

100%

tool-use events traced

sandbox tiers (low / medium / high)

end-to-end

agent red-team coverage

Frequently asked

Asked, answered, sourced.

LangGraph, LlamaIndex, CrewAI, AutoGen, OpenAI Assistants, and the Anthropic Agent SDK. Raw Python agents work via the SDK; MCP server integration is first-class.

Retrieved content is evaluated by the same injection classifier as user input. Suspicious content is stripped or flagged before being injected into the prompt.

Yes. Tool allowlists (per-agent, per-tenant), argument validation, and runtime egress policies. High-risk tools can require human-in-the-loop approval.