AI Agent Fundamentals Rules Best Practices

This page distills every rule list in the Agent Fundamentals Rules section into one standalone checklist - positive, enforceable practices for scope, stopping, context, and model policy.

It is meant for design reviews, production-readiness gates, and periodic audits - not as a one-time read.

How to Use This Checklist

• Work by category (A-F); new teams usually start with A and B before deep model routing.
• Require evidence for each checked item - a config value, code path, ADR, or eval - not a vibe.
• Use as a gate before any multi-turn agent reaches real users, real budget, or write tools.
• Revisit after incidents and whenever autonomy or tool surface expands.

A - Scope Fitness

• Confirm the problem needs mid-run branching or tool choice. Fixed paths stay scripts or workflows, not open loops.
• Write a pass/fail goal and an out-of-scope list before coding the loop. Done and not-done are reviewable artifacts.
• Match autonomy to reversibility and blast radius. Read-only or human-gated modes come before unsupervised writes.
• Cap the tool inventory to the stated goal. Extra tools require an explicit scope change.
• Refuse multi-agent splits until a single agent is bounded and measured. Complexity waits for fundamentals.

B - Stopping Conditions

• Enforce a hard max-turn ceiling in runtime code. Prompts alone do not stop loops.
• Enforce a wall-clock timeout for the whole run and for tools. Hung I/O cannot hide inside turn counts.
• Define success with a checkable predicate or oracle when possible. Free-text "done" is not enough for executable goals.
• Detect no-progress (repeated tool signatures or empty deltas) and stop or escalate. Polite infinite loops count as failure.
• Expose machine-readable stop reasons to logs and UX. Silent death is not an acceptable terminal state.

C - Context Discipline

• Structure stable policy into labeled sections with owners for changes. Unreviewed accretion is forbidden by default.
• Keep volatile facts out of the stable system policy blob. Dynamic data lives in turn state.
• Trim or summarize stale tool results on an explicit policy. Markers note omissions; dumps do not grow forever.
• Truncate or summarize oversized tool payloads at the adapter boundary. The model never receives unbounded raw responses by default.
• Restate the active goal at major phase boundaries on long runs. Local tool subgoals do not replace the product goal.

D - Model Policy and Cost

• Resolve models through logical policies or tiers, not scattered raw ids. Call sites request capability, not a hard-coded string.
• Default to the cheapest tier that meets the quality bar. Escalation requires sample evidence.
• Support fallback and runtime override without rewriting business logic. Outages and experiments stay config-level.
• Log resolved model id, policy name, and token usage per run. Cost and quality retros need facts.
• Re-eval tool-calling quality whenever the model map changes. Tiers are not interchangeable without proof.

E - Reliability Hygiene

• Classify tool errors before retrying. Not every failure deserves another turn.
• Prefer human handoff over silent infinite retry on high-stakes paths. Escalation is a designed outcome.
• Keep a minimal golden eval set for each agent. Even small oracles beat vibe-only shipping.
• Trace each perceive-reason-act cycle for failing runs. Unobservable loops are undebuggable.
• Environment-gate any "debug unbounded" modes. Production always remains bounded.

F - Team and Governance

• Assign an owner for fundamentals defaults (stops, scope template, model policy). Standards without owners drift.
• Turn each incident class into a rule or checklist item. One-off patches are not enough.
• Review fundamentals when autonomy, tools, or routing change - not only after outages. Cadence prevents surprise.
• Require documented exceptions for any rule override. Silent local hardcodes are decision drift.
• Keep the rule set small enough to remember and enforce. A manifesto nobody checks is not a control.

FAQs

Related

• Why Fundamentals Rules Prevent Early Agent Project Failures - why these checkboxes exist.
• The Core Agent Fundamentals Rules: A Quick-Reference List - table form with deep links.
• Scope Rules: When a Problem Actually Needs an Agent - detail behind category A.
• Stopping-Condition Rules Every Agent Must Implement - detail behind category B.
• Context Discipline Rules for Predictable Agent Behavior - detail behind category C.
• Model-Swapping Rules for Cost and Capability Flexibility - detail behind category D.
• Common Fundamentals Mistakes New Agent Builders Make - negative patterns these practices prevent.

Stack versions: Pins from the category manifest (verify at build): OpenRouter (~315+ models, July 2026 pricing/fees); LangGraph 1.0+; CrewAI 1.14+; Microsoft Agent Framework 1.0; Vercel AI SDK 6; Pydantic AI (latest); LlamaIndex (latest); OpenAI Agents SDK (latest + MCP); MCP (Linux Foundation governance); A2A (HTTP+SSE+JSON-RPC 2.0); Solana @solana/web3.js + @solana/spl-token.