1) Choose the Right Use Case Great candidates

High volume, repetitive tasks with clear outcomes (e.g., triage tickets, draft responses, QA checks) Multi-step workflows that require decisions across several data sources/tools Processes already documented with SOPs that can become agent policies Avoid (at first) Open-ended tasks without objective success criteria Tasks with large, unmitigated risk if wrong (compliance, finance) unless tightly gated Workflows with poor or inaccessible data Define success Write a crisp acceptance test for the one thing you’ll automate first: Input: What the agent receives (formats, examples) Output: Exact required result (schema, tone, constraints) Quality bar: How you’ll check it (rules, regexes, eval set) SLOs: Latency target, cost ceiling, success rate

2) Map the Workflow Break the process into states and decisions: Trigger → What starts this? (webhook, cron, queue message) Gather → Which data is needed? How to fetch it safely? Plan → Which sub-steps are required? In what order? Act → Which tools will be called? With what arguments? Verify → Did the result satisfy policy/acceptance tests? Escalate → When and how to hand off to a human? Log → What telemetry do we keep (inputs/outputs, tool calls, costs)? Finish → Where do we write back the result? Make a short SOP the agent can follow. If there’s no SOP, you’re not ready—write it first.

3) Reference Architecture (Pragmatic) Trigger Layer: webhook/queue scheduler Router/Planner: decides the next action (LLM + rules) Tool Adapters: APIs (CRM, ticketing, DB, search, email, Slack, internal services) Memory/State: short-term step context + long-term case history Policy/Guardrails: PII redaction, tool allowlist, rate limits, output validators Human-in-the-Loop (HITL): review/approval UI when risk or uncertainty is high Observability: traces of prompts, tool calls, costs, latency, success metrics Storage: logs, artifacts, final outputs Tip: start with one agent that can plan → call tool → verify → loop. Add multi-agent patterns later only if needed.

4) Data, Tools, and Access

Connect the minimum tools first (read-only if possible). Use narrow scopes and allowlists for each tool. Normalize outputs into a structured schema the agent can reason about. Add caching for frequent reads; backoff & retry on flaky APIs. For private data, apply row/field-level security and redaction before the model sees it.

5) Prompts & Policies (Make the agent predictable)

System prompt skeleton You are an operations agent that resolves . Follow the SOP exactly.

• Only use allowed tools.
• Never fabricate IDs or data.
• If acceptance tests fail or confidence is low, escalate. Return JSON following this schema: .

SOP snippet Step 1: Validate input fields {A,B,C}. If missing → request/flag. Step 2: Fetch record from CRM by {ID}. If not found → escalate. Step 3: Draft update using template T; keep under 120 words; no claims without source. Step 4: Run validator V; if fails → fix once; else escalate. Step 5: Write back to system and post summary to Slack channel #ops. Output contract (JSON) { "decision": "proceed|escalate", "actions": [ {"tool": "crm.update", "args": {...}, "result_ref": "r1"} ], "summary": "string <= 120 chars", "evidence": ["source://crm/123", "source://email/456"], "validation": {"passed": true, "errors": []} }

6) Guardrails That Actually Help

Input filters: block PII leakage, unsupported languages, oversized payloads Tool gating: explicit allowlist; dry-run mode in staging Deterministic checks: regex/JSON schema validators, business rules Cost & time caps: limit steps, tool calls, and tokens per run Escalation rules: confidence < threshold, validator fail, ambiguous user intent, high-risk actions Audit trail: immutable logs (prompts, tool IO, diffs, human approvals)

7) Evaluation Before Launch Create a small eval set (20–100 real past cases): Success rate (met acceptance test without HITL) Intervention rate (needs human) Error types (reasoning, tool, data, policy) Latency (P50/P95) and cost per task Hallucination proxy: fact checks vs. ground truth fields Automate this: run your agent on the eval set after every change. Ship only when it beats the baseline (e.g., existing manual SLAs).

8) Deployment & Rollout Staging with shadow traffic (read-only tools) Limited write behind a feature flag; HITL required Progressive exposure (by team, customer segment, or time window) SLOs & alerts: success rate, error spikes, tool failure, cost anomalies Runbooks: how to pause the agent, drain queues, and revert model/prompt versions

9) Operating the Agent Daily: Check dashboards (success rate, escalations, costs) Weekly: Review 10 random traces; tag failure causes; update SOP/prompt Monthly: Retrain/rerank retrieval corpus, rotate keys, prune tools you don’t use Postmortems: Treat incidents like software—root cause, fix forward, add tests

10) Measuring ROI (Simple and honest) Time saved = (manual minutes per task − agent minutes of HITL) × volume Quality delta = fewer defects/reopens × cost of defect Coverage = % cases handled outside business hours or in more languages Cost to serve = model + infra + tool calls + HITL time Ship the cheapest agent that clears the bar, not the fanciest.

11) Example: Support Ticket Triage Agent Goal: Auto-label priority & route tickets to the right queue. Inputs: Subject, body, product, customer tier. Tools: Knowledge base search (read), CRM (read), Ticketing API (write: label & route). Acceptance test: Matches human labels on eval set ≥ 90%; P95 latency ≤ 5s; ≤ 10% escalations.

Flow

Validate fields; normalize text. Retrieve 3 relevant KB articles. Infer priority using rules + LLM reasoning. Choose queue from taxonomy; justify with evidence. Validate output schema; if missing evidence → escalate. Apply labels; post 2-sentence internal note with reason. Metrics after pilot (illustrative) Success 92%, escalations 8% Median latency 2.2s, cost $0.007/ticket Reopen rate down 14%

12) Implementation Checklist

One narrow use case + acceptance test written Tool allowlist with least-privilege credentials System prompt + SOP + JSON schema Validators (schema + business rules) HITL path + approval UI Telemetry (traces, cost, latency, outcomes) Eval set & automated regression tests Rollout plan + SLOs + alerting Runbook & incident response Governance: versioning, audit, data handling

13) Template: Incident-Safe Escalation Note (Agent → Human)

Why I’m escalating: Validation failed on step 4 (no CRM record for ID=123). What I did: Retrieved email headers, searched CRM by email + domain, checked recent tickets. My best next action (not executed): Create provisional contact and attach ticket. What I need from you: Confirm the correct customer record or approve provisional creation. Trace ID: 8f2a…c9

14) Common Pitfalls

“Let’s build a general-purpose agent” → scope creep; start with one task. No ground truth → impossible to measure improvement. Too many tools on day 1 → more failure modes than value. Ignoring cost observability → surprise bills. Skipping HITL → brittle behavior on edge cases.

15) Where to Go Next

Add structured retrieval (field-aware search) for better grounding. Introduce skills as modular tool bundles (e.g., “billing lookup”, “KB cite”). Explore multi-agent only when you can prove single-agent planning is the bottleneck.https://nextbrowser.com/