The failure modes we see across teams running agents in real workloads — and the inline mitigation that actually stops each one.
The agent invents a customer ID, a SKU, an email address, or a refund amount and calls a real tool with it. The model is confident, the arguments parse, the tool fires.
Validate every tool argument against a schema and a live data check before execution. Block calls referencing entities that don't exist. Require approval for destructive verbs (delete, refund, deploy).
An agent gets stuck. It re-plans, re-tries, re-emails the same lead twelve times in five minutes, or hammers a downstream API until rate limits trip and on-call wakes up.
Loop detection on tool-call signatures, per-agent call budgets, and circuit breakers that trip on repeated identical actions. Halt the run, page no one, log everything.
User content, scraped pages, email bodies, or tool results contain instructions that hijack the agent — exfiltrate data, call the wrong tool, ignore previous policy.
Don't trust the prompt to enforce safety. Constrain the action surface: tool allowlists per context, argument-level policies, and human approval for any tool that touches sensitive data.
The agent reads a sensitive record, then helpfully includes it in an outbound email, a webhook payload, or a debug log shared with a third party.
Tag fields as sensitive at the source. Validate outbound tool arguments against the tags. Block egress that mixes sensitive reads with external sends, even if the model 'meant well'.
A misconfigured agent burns $4,000 in OpenAI credits overnight, or a tool retries on failure and racks up Twilio charges nobody budgeted for.
Per-agent and per-tool cost ceilings enforced inline. Soft cap pages a human; hard cap halts the agent. Cost is a first-class signal, not a monthly invoice surprise.
A model upgrade or prompt tweak ships. Quality drops 8%, the agent now picks the wrong tool 1 in 50 runs, and you find out from a customer ticket two weeks later.
Per-agent reliability scoring tied to policy decisions and tool outcomes. Anomaly alerts when block rate, retry rate, or approval rate moves outside its baseline.
Something went wrong. The user is upset. Your logs show 600 LLM tokens and a tool error. You can't reconstruct what the agent saw, planned, or decided.
Capture the full agent run — model output, tool selected, arguments, policy decision, tool result — as a replayable timeline. Every incident becomes a click, not an investigation.
Audit asks who approved this action, what the policy was at the time, and whether the data the agent touched left the region. The honest answer is 'we don't know'.
Tamper-evident, append-only action log with policy version, approver, inputs, outputs, and decision per call. The control evidence is generated automatically as agents run.
Add SafeRun in three lines. Validate, block, and replay every risky tool call — before it touches production.
