There is no best agent, only the right agent for one workflow, one data reality, and one risk appetite. This chapter is the design method: discovery, autonomy, architecture mapping, and a spec you can hand to a builder.
Discovery: ten questions before any code
Most failed agent projects were lost before engineering began, wrong workflow, fuzzy success criteria, or data that wasn't there. Run discovery as a working session with the people who do the job today, and leave with written answers to:
- What exact workflow, end to end? Walk a real example, not the org chart's version of it.
- How often does it run, and what does each run cost today in time and money?
- What does a failure cost, and is it reversible? (A wrong draft is cheap; a wrong payment is not.)
- Where does the knowledge live, systems, documents, or someone's head?
- Which systems must the agent read or write, and do APIs exist?
- What does 'done well' mean, measurably? This sentence becomes your eval rubric.
- Who reviews, who approves, who gets the escalations?
- What data may leave the building, and what must never? (Residency rules decide architecture.)
- What volume in 12 months if it works? Build for that, not for the demo.
- Who owns the agent after launch, its prompts, evals, and weekly flywheel?
Pick the autonomy level deliberately
Autonomy is a dial, not a binary, and the right setting comes from failure cost and trust earned, not ambition. Ship one level below where you think you belong, instrument everything, and earn your way up with eval evidence.
rising autonomy → rising blast radius → rising need for evals, budgets and audit
L5 L4 L3 L0
Scripted automation no model in the loop
L2 L1
Assist drafts & suggestions; human does the work Approve agent acts after explicit human sign-off Supervise agent acts; human reviews samples & exceptions Delegate agent owns the task; escalates by policy
Figure 12.1. The autonomy ladder. Most successful first deployments launch at L2-L3.
From answers to architecture
Discovery answers map almost mechanically onto the choices from Parts II and III:
Autonomous agent owns the outcome end-to-end Discovery finding Design consequence Where Predictable process, steps known workflow with LLM steps, not a free agent Ch. 2 Open-ended, branching, judgment-heavy agent loop; add planning + reflection Ch. 2 Multiple systems to touch MCP servers per system; typed tool contracts Ch. 4 Needs to remember users/cases over time scoped memory layer + write policy Ch. 5 Pause for approvals; long-running durable execution, checkpoints, HITL gates Ch. 6 Strict data residency local/hybrid serving; self-hosted gateway & tracing Ch. 7-8 High volume, cost-sensitive caching + cascade routing from day one Ch. 9 Irreversible or high-value actions L2-L3 autonomy, approval gates, budgets Ch. 10 Quality disputes likely eval suite + tracing before launch, not after Ch. 11
Build, buy, or assemble
Buy a finished product when your workflow is genuinely commodity (generic meeting notes, first-line IT FAQ) and differentiation doesn't matter. Build on frameworks plus your own interfaces when the workflow is your business, your pricing logic, your service playbook, your data. The middle path, assembling vendor agents behind protocol seams (MCP for tools, A2A between agents), is increasingly the pragmatic default: buy the commodity edges, build the differentiating core. Whatever you choose, the evals, budgets and audit trail are always yours to own.
Worked spec, a real-estate lead qualifier
A brokerage receives hundreds of portal and WhatsApp enquiries weekly; agents waste hours on unqualified leads and respond slowly to good ones. Discovery says: high volume, modest failure cost (a misrouted lead), bilingual audience, CRM is the system of record, response speed is the KPI. The spec that falls out:
- Objective, respond to every enquiry in under 2 minutes, qualify against budget / area / timeline / financing, and book viewings for qualified leads.
- Autonomy, L3, messages send automatically; pricing commitments and complaints escalate to a human within the same thread.
- Pattern, router + single agent loop; no multi-agent topology needed at this volume.
- Tools (via MCP), CRM read/write, listings search, calendar booking, WhatsApp Business send, each schema-validated, send-rate budgeted.
- Memory, per-lead profile (facts + preferences) with 12-month decay; no cross-lead recall by policy.
- Models, budget model for classification and extraction; frontier model for negotiation-tone drafting; prompt caching on the listing-policy prefix.
- Evals, 40 labelled historical enquiries, qualification accuracy ≥ 90%, zero pricing commitments, Arabic quality spot-checked by a native speaker.
- Success metric, median response < 2 min; ≥ 25% more viewings booked per 100 enquiries within 8 weeks, at agreed cost per lead.
The one-page agent spec
One page, eight headings: Objective · Autonomy level · Pattern · Tools & data · Memory policy · Models & cost plan · Eval set & pass bar · Owner & escalation path. If you cannot fill all eight, you are not ready to build — you are ready for more discovery.