HireNinja: Blog

Plan overview

• Define what “good” looks like: support agent SLOs, guardrails, and blast radius.
• Harden prompts, scope tools/permissions, and sandbox access.
• Automate red teaming with benchmark attacks and OpenAI-style agent evals.
• Instrument everything with OpenTelemetry (gen-ai) for ASR, costs, and drift.
• Ship a remediation loop: patch prompts/policies, re-run tests, and promote safely.

Agent platforms are racing ahead—Microsoft’s Agent 365, Google’s Mariner, Anthropic’s Claude for Chrome, and OpenAI’s AgentKit—while venture dollars are pushing customer-facing AI agents into production. That pressure makes safe-by-default support agents non‑negotiable. This guide gives founders and support leaders a 48‑hour playbook to red‑team a helpdesk/chat/WhatsApp support agent and ship with confidence.

Who this is for

Startup founders, support ops leaders, and AI platform teams running agents that answer tickets, process returns, issue refunds, or triage order problems across chat, email, or WhatsApp.

Prerequisites (2–4 hours)

1. Write 3–5 Agent SLOs (e.g., Success rate ≥ 95%, Refund-policy violations ≤ 0.5%, Time‑to‑first‑token ≤ 1.5s, Cost per resolved ticket ≤ $0.25). If you need a template, see our internal guide: Ship Agent SLOs That Matter.
2. Register the agent with an MCP‑style agent registry (identity, capabilities, policies, secrets). Starter patterns here: Build an Agent Registry.
3. Enable observability with OpenTelemetry’s emerging gen‑ai conventions to capture requests, token usage, tool calls, and outcomes.

Day 1 — Hardening + Test Harness

Morning: Lock down behavior and blast radius

• Principle of least privilege: Give the agent only the tools it needs (e.g., create_refund up to $50, read_orders, issue_coupon), split read/write keys, and disable anything unrelated (e.g., email send).
• Sandbox everything: Stage environment, fake payment rails, and non‑production PII. Log all tool outputs.
• System prompt guardrails: Explicitly forbid off‑policy actions, define escalation triggers, and instruct the agent to ignore instructions inside retrieved data (“prompt injection in tool outputs”).
• Defensive retrieval: Tag data sources. Instruct the agent to treat all retrieved content as untrusted and to require a human or policy check for actions with real‑world impact.

Afternoon: Build the red‑team harness

• Attack library: Prepare direct and indirect prompt injections (in tool outputs), refund‑abuse attempts, data‑exfil probes (“print last 10 credit cards”), and vendor‑impersonation emails. See Microsoft’s guidance on indirect prompt injection and agentic risk categories for inspiration. Reference.
• Automated evals: If you’re on OpenAI’s platform, wire up Evals for Agents style tests to score policy compliance, step‑level traces, and tool‑use outcomes; otherwise, mirror the pattern with your stack.
• Metrics to capture: Attack Success Rate (ASR), jailbreak rate, policy‑violation rate, false escalation rate, mean/95p TTFT, cost per resolved ticket.

Day 2 — Run Attacks, Measure, Fix, Repeat

Morning: Execute automated red teaming

1. Batch runs: Execute 100–300 red‑team scenarios across channels (site widget, email, WhatsApp). Randomize model, temperature, and tool latency for realism.
2. Score and cluster failures: Group by attack type (direct vs indirect), policy violated (refund limit, PII leak), and tool misuse (dangerous action without approval).
3. Patch 1: Fix prompts/policies for the top 3 failure clusters; add allow/deny lists; tighten tool scopes; add human‑in‑the‑loop when confidence y.

Afternoon: Instrument and operationalize

1. Trace agents with OpenTelemetry: Emit gen_ai spans/metrics for every step: model calls, tool invocations, memory reads/writes. Capture attributes like gen_ai.operation.name, gen_ai.provider.name, gen_ai.response.finish_reasons, and custom agent.policy.outcome.
2. Dashboards and alerts: Visualize ASR, policy violations, and cost per ticket. Page on ASR spikes or sudden cost drift. Tie alerts to auto‑rollback of risky prompt changes.
3. Patch 2 + re‑test: Re‑run the same attack set. If ASR ≤ target and SLOs are green, promote to production.

Starter code: minimal OpenTelemetry for agent spans

# pip install opentelemetry-sdk opentelemetry-exporter-otlp
from opentelemetry import trace
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor
from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter

provider = TracerProvider()
provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))
trace.set_tracer_provider(provider)
tracer = trace.get_tracer("support-agent")

# Wrap a model call
with tracer.start_as_current_span("chat openai:gpt-4.1-mini") as span:
    span.set_attribute("gen_ai.operation.name", "chat")
    span.set_attribute("gen_ai.provider.name", "openai")
    span.set_attribute("agent.task.name", "refund_request")
    # ... call model, record token usage, tool names, and outcomes

See OpenTelemetry’s evolving gen‑ai conventions for spans and metrics for more attributes you can use.

Attack checklist (copy/paste)

• Direct injection: User asks the agent to ignore policy and issue full refund.
• Indirect injection: Malicious instructions hidden in an email or order note retrieved via tools.
• Data exfiltration: Attempts to dump PII or payment data via broad queries.
• Refund abuse: Multiple small refunds to exceed limits, or “test” charges.
• Vendor impersonation: Fake “CEO asks for urgent coupon issuance.”
• Model drift: Behavior changes after model/temperature swap.

Scorecard template

Metric	Target	Result	Status
Attack Success Rate (ASR)	<= 2%	—	🟡
Policy‑violation rate	<= 0.5%	—	🟡
False escalations	<= 3%	—	🟡
TTFT p95	<= 1.5s	—	🟡
Cost per resolved ticket	<= $0.25	—	🟡

Why this matters now

Enterprise push toward fleet management (e.g., Agent 365) and real web/browser agents increases the attack surface. Research and industry competitions consistently show modern agents remain vulnerable to prompt injection and policy violations, especially via indirect attacks. Don’t wait for a real incident—bake red teaming and observability into your rollout.

Further reading and context

• Agent platforms and trends: Agent 365, Project Mariner, Claude for Chrome, AgentKit.
• Security evidence: AgentXploit, Large‑scale agent red‑teaming competition (ART), Microsoft’s AI Red Teaming Agent.
• Observability standards: OTel gen‑ai spans and metrics.
• Related HireNinja playbooks: 48‑Hour AI Agent Governance Checklist, AI Agent FinOps (cut costs 25–40%).

Call to action

If you’re days from launch or BFCM traffic, run this 48‑hour red team now. Need help? Subscribe for weekly agent ops playbooks—or book a HireNinja consult to harden your support agent, end‑to‑end.