Agent Reliability Engineering: SLOs, Runbooks, and Incident Response for AI Agents in 30 Days (MCP + OpenTelemetry)

AI agents are moving from demos to daily work. Microsoft launched Agent 365, a control plane to manage fleets of enterprise agents, and OpenAI shipped AgentKit to build and evaluate them. At the same time, researchers are finding surprising failure modes in realistic tests—see Microsoft’s synthetic marketplace where agents failed in unexpected ways. If you’re a founder or operator, you don’t just need agents—you need Agent Reliability Engineering (ARE).

This 30‑day, vendor‑agnostic playbook shows how to ship SLOs, runbooks, and incident response for AI agents using OpenTelemetry GenAI semantic conventions, MCP security best practices, and continuous evals.

What is Agent Reliability Engineering?

ARE applies SRE principles to AI agents: measurable reliability (SLIs/SLOs), defense‑in‑depth guardrails, fast detection, and practiced incident response. It complements your agent control plane and registry (identity, policy, audit) and turns “hope it works” into “we can prove it.”

If you’re rolling out an enterprise platform like Agent 365 or Salesforce’s Agentforce 360, or building on AgentKit, an ARE foundation prevents surprises, contains blast radius, and accelerates scale‑up.

The 30‑Day ARE Playbook

Week 1 — Inventory, Baselines, and SLIs/SLOs

• Centralize your agent inventory with owner, purpose, tools, data scope, and environments. If you don’t have this yet, stand up a lightweight registry. Our 7‑day guide: Agent Registry + IAM.
• Define SLIs/SLOs for each business flow. Start with:
  • Task success rate (golden paths)
  • Mean time to correct action (TTCA)
  • Tool error rate (API/browser tool failures)
  • Escalation rate to human
  • Safety fallback rate (refusals/guardrail triggers)
  • Unit economics: cost per successful task
• Instrument agents with OpenTelemetry GenAI agent spans and GenAI metrics (e.g., gen_ai.client.token.usage, error counters, latency histograms). Emit agent and tool spans so you can correlate failures to specific tools.

Week 2 — Observability, Budgets, and Evals

• Dashboards & alerts: Wire SLIs to dashboards and page on SLO breaches. Alert on abnormal spikes in tool errors, long tail latencies, and safety fallbacks.
• Cost guardrails: Set budgets per agent and per flow; alert on cost per success regression. Use our 30‑60‑90 FinOps plan: Agent FinOps.
• Continuous evals: Add pre‑merge and nightly evaluations using OpenAI Agent Evals for trace grading and dataset‑based scoring. Fail builds if eval scores drop beyond thresholds.

Week 3 — Guardrails, Security, and Runbooks

• Security baseline (MCP): Apply MCP security best practices—audience‑bound tokens, scope minimization, sandboxed local servers, and explicit user consent for one‑click configuration. See our 30‑Day Security Hardening Plan.
• Runbooks: For each top incident, write a two‑page max runbook: how to detect, first actions, rollback, and escalation. Consider auto‑scribe/IR tooling patterns (e.g., AWS documents an AI investigative agent approach) to capture timelines without toil.
• Change policy: Require eval pass + canary + SLO no‑regression for any agent prompt/graph change. Log every change into your registry for audit.

Week 4 — Game Days, Red Teaming, and Handoffs

• Game day drills: Recreate Microsoft’s findings by simulating adversarial marketplaces and misaligned incentives; measure detection and MTTR. Their research shows why agents can fail in realistic conditions—prepare accordingly (study summary).
• Red‑team your support agent: Follow our 48‑hour plan to probe prompt injection and tool abuse: red‑teaming guide.
• On‑call & postmortems: Assign owners, define clear severity levels, and adopt blameless postmortems with links to traces, evals, and cost deltas.

SLIs/SLOs that Matter for Agents

• Task success rate (TSR): ≥ 95% on golden paths (checkout recovery, refund approval, password reset).
• TTCA (mean time to correct action): ≤ 8s for API‑only, ≤ 20s for browser agents.
• Tool error rate: ≤ 0.5% per 1,000 calls to critical tools.
• Safety fallback rate: ≤ 2% with reason codes logged.
• Cost per successful task: baseline now; expect 30–50% reduction with FinOps controls over 90 days.

Observability: What to Emit

Use the GenAI semantic conventions so your telemetry is portable:

• Spans: agent (workflow), model (LLM calls), tool (API/browser actions)
• Metrics: gen_ai.client.token.usage, gen_ai.request.duration, gen_ai.errors, custom agent.task.success
• Events: input/output truncation, safety policy triggers, escalation

References: GenAI agent spans and GenAI metrics.

Security: Reduce Blast Radius Before It Matters

Two realities:

1. Identity fragmentation around MCP servers is a real risk without firm IAM (analysis).
2. Attackers will exploit weak consent and over‑broad scopes (MCP guidance).

Minimums for Week 3:

• Issue agent identities; bind tokens to audiences; enforce least privilege.
• Require human approval for new tool scopes; expire unused grants.
• Log all agent actions and tool calls; keep 30–90 days searchable.

If you want third‑party options, the market is maturing—security startups are emerging specifically for MCP agent fleets (recent funding).

Runbook Template (copy/paste)

Title: [Agent/Flow] Incident Runbook — [Short name]
Severity Levels: SEV1 (customer impact), SEV2 (degraded), SEV3 (internal only)

Detect
- SLO breach alert: [link]
- High safety fallback rate or tool errors: [query]

First Actions (10–15 minutes)
- Toggle safe mode (read-only or human-in-loop)
- Roll back to last good agent version / prompt
- Drain traffic to canary; pin model/version

Diagnose
- Check trace sample: [link]
- Compare eval deltas (last 24h): [dashboard]
- Inspect tool errors: [query]

Mitigate
- Apply known fixes or switch to backup tool
- Escalate to human workflow if TSR < SLO for 15 min

Comms
- Stakeholders channel + customer update template

Postmortem
- Root cause, contributing factors, lessons, actions, owner & due date

Tooling Stack (reference)

• Control plane & registry: ship in 7 days
• Evals: OpenAI Agent Evals
• Observability: OpenTelemetry SDK + Collector; GenAI conventions
• Security: MCP best practices; policy‑as‑code; scoped credentials

Why This Matters Now

Enterprises expect an “agent OS” experience. Microsoft’s Agent 365 signals that operators will manage agents like employees—identity, access, audits, the whole stack (Wired). Without ARE, you’ll scale incidents, not impact.

Next Steps

1. Stand up the registry + SLOs this week (7‑day plan).
2. Add GenAI telemetry + dashboards; enable alerts.
3. Integrate evals into CI; block risky changes.
4. Write runbooks for your top 3 incidents; schedule a game day.
5. Harden security and budgets (security plan · FinOps playbook).

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Call‑to‑Action: Want a ready‑to‑use ARE checklist and runbook templates? Subscribe to HireNinja for weekly agent playbooks, or reach out if you want help instrumenting OpenTelemetry and evals across your agent stack.