name: agent-onboarding-checklist description: Use when deploying a new agent into production. Checklist for onboarding agents. author: Melisia Archimedes url: https://hivedoctrine.com mcp: https://hive-doctrine-mcp.vercel.app/mcp

title: "Agent Onboarding Checklist: Your First 7 Days" author: Melisia Archimedes collection: C4 Infrastructure tier: pollen price: free version: 1.0 last_updated: 2026-03-09 audience: agent_operators hive_doctrine_id: HD-1018 sources_researched: [agent deployment guides, production onboarding playbooks, DevOps best practices, operator community forums] word_count: 942

Agent Onboarding Checklist: Your First 7 Days

Most people try to build an agent in an afternoon. The good ones take a week.

This is your roadmap from blank canvas to production-ready agent. Seven days. Seven milestones. Each day has a clear objective, a checklist, and a gate you must pass before moving forward.

The 7-Day Framework

You're not optimising for speed here—you're optimising for confidence. By day 7, you'll know whether your agent works, what it costs, where it breaks, and what's next. You'll have production logs. You'll have failure patterns. You'll have a go/no-go decision backed by data.

The framework splits into three phases:

1. Foundation (Days 1–3): Identity, model selection, system prompt, tool access boundaries
2. Integration (Days 4–5): MCP connections, tool testing, task validation
3. Validation (Days 6–7): Monitoring setup, staging deployment, launch readiness

Day-by-Day Checklist

Day 1: Define Purpose, Pick Your Model, Set Up API Access

Objective: Know what your agent does. Have credentials ready.

• Write your agent's SOUL.md (what it is, what it's not, non-negotiables)
• Document the primary use case in one sentence
• List 3–5 secondary use cases (things it could do but shouldn't)
• Choose your base model (Claude 3.5 Sonnet? Claude Haiku? GPT-4o? Llama?)
  • Document your reasoning: latency budget, cost-per-call, accuracy requirements
• Create API credentials for your chosen provider(s)
• Test authentication: run a single API call, verify response time and cost
• Set up a secrets manager or .env file (never hardcode keys)
• Document your model's context window and cost-per-1k tokens
• Review the model's instruction-following ability for your use case

Gate: You have a SOUL.md, API credentials that work, and you've validated a single API call in under 100ms.

Day 2: Write System Prompt, Define Tool Access, Set Boundaries

Objective: Your agent knows its constraints.

• Write a detailed system prompt (500–800 words)
  • What the agent is and why it exists
  • What it must not do
  • How it should handle errors, edge cases, user hostility
  • Tone and voice (clinical? friendly? urgent?)
• Define which tools the agent can call
  • Create a whitelist (not a blacklist)
  • Specify role-based access (can it modify production? Can it delete?)
• Write tool descriptions: what each tool does, when to use it, what could go wrong
• Set hard limits:
  • Max tool calls per session
  • Max API cost per interaction
  • Timeout thresholds (e.g., "if a tool takes >30s, fail fast")
  • Rate-limiting rules
• Design your failure mode playbook:
  • Agent hallucinating tool outputs → How do you detect and stop it?
  • Tool returning null/error → Agent fallback strategy?
  • Budget overrun → Kill switch or graceful degradation?
• Document your decision log (why you set boundaries this way)

Gate: System prompt is written and reviewed. Tool access is defined. Boundaries are enforced in code (not just documentation).

Day 3: Build the Memory Layer, Choose Context Strategy

Objective: Your agent remembers what matters.

• Choose your memory architecture:
  • Context window: Keep everything in the prompt (cheap, simple, limited to 8–200k tokens)
  • Sliding buffer: Keep N most recent interactions + fixed episodic summary
  • Vector store: Embed all interactions, retrieve relevant context on each call
• Implement your chosen strategy
• Test memory retrieval under load (does it find the right context?)
• Design your summary function (if using buffer/vector):
  • How do you compress a 10-turn conversation into a 2-sentence summary?
  • What information is most valuable to keep?
• Set memory retention policy:
  • How long do you keep logs? (24 hours? 30 days? Forever?)
  • Do you store personally identifiable data? (Decide before day 1 of production)
• Implement cost tracking: log memory retrieval cost separately from inference cost

Gate: Memory layer is implemented, tested under realistic load, and cost-tracked.

Day 4: Connect Tools via MCP, Test Individually

Objective: Your agent's hands work.

• Set up Model Context Protocol (MCP) server (or HTTP tool wrapper)
• For each tool:
  • Write the MCP/HTTP schema (inputs, outputs, errors)
  • Test the tool in isolation (don't call agent yet)
  • Document failure modes
  • Verify timeout behaviour
  • Check rate-limiting
• Run a "tool gauntlet" test:
  • Happy path: call each tool with valid inputs
  • Error cases: invalid inputs, rate-limit conditions, timeout
  • Edge cases: empty results, malformed responses, slow responses (>10s)
• Log all tool calls: timestamp, input, output, latency, cost
• Create a tool status dashboard (can you see which tools are slow/expensive?)

Gate: All tools pass the gauntlet. You have a tool status dashboard. Zero silent failures.

Day 5: Run 50 Test Tasks, Score Performance

Objective: Measure what works and what breaks.

• Design 50 test tasks covering:
  • Happy path (30 tasks): typical use cases, realistic inputs
  • Error cases (10 tasks): malformed input, missing data, edge cases
  • Boundary cases (10 tasks): maximum complexity, maximum scope, ambiguous requests
• Run all 50 tasks, log outputs:
  • Task ID, input, output, latency, cost, success/failure, failure reason
• Calculate your scorecard:
  • Completion rate: % of tasks that succeeded (target: ≥90%)
  • Accuracy: % of successful tasks that were correct (target: ≥95%)
  • Cost per task: total spend ÷ 50 (budget check)
  • P95 latency: 95th percentile response time
• Identify failure patterns:
  • Did certain task types fail more often? (e.g., ambiguous requests?)
  • Did certain tools fail more often?
  • Did the agent misuse a tool?
• Tune system prompt or tool definitions based on failures

Gate: Completion rate ≥85%, accuracy ≥90%, cost within budget, failure patterns documented and addressed.

Day 6: Add Monitoring, Set Up Logging and Alerts

Objective: You know when your agent is breaking.

• Set up structured logging (every agent action is logged as JSON)
  • Timestamp, agent ID, task ID, action, result, duration, cost
• Create monitoring dashboards:
  • Success rate (rolling 1-hour, 24-hour windows)
  • Cost trend (per hour, per day)
  • Latency distribution (p50, p95, p99)
  • Error rate by type (tool failed, timeout, hallucination, budget exceeded)
• Define alert thresholds:
  • Success rate drops below 85% in last hour → page on-call
  • Cost per task exceeds budget by 2x → warn
  • P95 latency exceeds threshold → warn
  • Any "hallucination" detected → alert
• Set up log rotation (logs can grow unbounded)
• Test your alerting system with a simulated failure

Gate: Monitoring is live. You can see success rate, cost, and latency in real-time. Alerts are tested and firing.

Day 7: Deploy to Staging, Run 24 Hours, Go/No-Go Decision

Objective: Validate in the wild before production.

• Deploy agent to staging environment (not production)
• Run realistic production-like traffic for 24 hours
  • Mix of happy path, edge cases, and error conditions
  • Similar load to what you expect in production
• Monitor continuously:
  • Are alerts firing? Are they useful or noisy?
  • Are logs parsing correctly?
  • Is cost tracking accurate?
• Review the 24-hour report:
  • 100 tasks completed. Success rate: X%. Accuracy: Y%. Cost: Z.
  • Any unexpected failures?
  • Any silent failures (success reported but output wrong)?
  • Any performance surprises?
• Make your go/no-go decision:
  • GO: Metrics meet thresholds. Failure modes understood. Ready for production.
  • NO-GO: Metrics below threshold. Failure mode unresolved. Return to day 2–5, fix, re-test.

Gate: 24-hour staging run complete. Go/no-go decision documented and signed off.

Go/No-Go Criteria

Your agent is GO for production if:

• ✅ Success rate ≥85% (tasks completed as intended)
• ✅ Accuracy ≥90% (correct outputs when task succeeds)
• ✅ Cost per task within budget (or you've justified the overage)
• ✅ P95 latency meets SLA (latency budget negotiated with stakeholders)
• ✅ Zero untraced failures (all errors logged and understood)
• ✅ Monitoring and alerting verified to work
• ✅ Runbook documented (how to restart, how to page on-call, how to rollback)
• ✅ SOUL.md, system prompt, tool schemas, and failure modes documented

If any box is unchecked, you're NO-GO. Fix the failing criterion. Return to the relevant day. Re-test. Re-gate.

What's Next: Days 8–30

Your 7-day checklist gets you to production. Days 8–30 are about learning and optimisation.

For the full 30-day playbook—including prompt engineering, tool optimisation, cost reduction, and scaling patterns—see the Agent Onboarding Playbook: Day 1 to Day 30 (Honey tier).

Days 8–14 focus on early production wins:

• Reducing cost per task by 30%
• Improving accuracy with targeted prompt tuning
• Adding new tools based on real-world failure patterns

Days 15–30 focus on scaling:

• Load testing (can your agent handle 10x traffic?)
• Multi-model strategies (when to use Haiku vs. Sonnet)
• Fine-tuning and caching (advanced optimisations)

Start with this 7-day checklist. Get to production. Then optimise.

Last updated: 2026-03-09 | Author: Melisia Archimedes | Hive Doctrine ID: HD-1018

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