Agents Best Practices is a reference skill and architectural guide for building agentic harnesses — the control plane that wraps a language model in a production system. It covers the full runtime: tool design, permissions, planning, memory, compaction, observability, and safety, across any domain.Documentation Index
Fetch the complete documentation index at: https://mintlify.com/DenisSergeevitch/agents-best-practices/llms.txt
Use this file to discover all available pages before exploring further.
Quickstart
Get the skill running in your agent in under 5 minutes
Installation
Install via npx skills, manual clone, or agent prompt
MVP Blueprint
Generate a domain-specific MVP harness from scratch
Architecture
Understand the component model and harness boundaries
What is an agent harness?
An agent harness is the runtime control plane that sits between a user request and a language model’s actions. The model proposes; the harness validates, authorizes, executes, records, and returns observations. Without a harness, a model that can call tools is an unaudited operator. This skill teaches you to design the harness correctly:- Model-tool-observation loop — how calls, results, retries, and stopping rules connect
- Typed tool registry — narrow, validated tools with declared risk classes
- Permission engine — reads, drafts, writes, and destructive actions get different paths
- Context builder — cache-aware assembly with retrieval and auto-compaction
- Planning and goals — when to plan, how to commit, when to stop
Who this is for
Agents Best Practices applies to any team building an agent with real-world side effects. The pattern is the same whether the domain is:- Software engineering and code review
- Research and data analysis
- Customer support and CRM workflows
- Finance, procurement, or legal review
- Healthcare, HR, or education workflows
- Operations, sales, or marketing automation
This is not only for coding agents. The harness discipline — loop, tools, permissions, planning, compaction, evals — is the same across every domain.
The central principle
“The model proposes actions; the harness validates, authorizes, executes, records, and returns observations.”Eight runtime rules follow from this:
- The harness acts, not the model — application code owns validation, authorization, execution, and logging
- Every tool call gets a result — denials, timeouts, and aborts are observations too
- Risk changes the loop — reads, drafts, writes, financial actions, and destructive actions need different permission paths
- Draft and commit are separate — high-risk side effects require approval records outside the prompt
- Context is built, not dumped — retrieve just enough, label trust boundaries, preserve active state across compaction
- Long-running work needs budgets — step, time, token, cost, and tool-call budgets are part of the product
- Skills and connectors are progressively disclosed — expose names first; load full workflows only when relevant
- Repeated failures become harness features — validators, tools, evals, and policies beat repeating prompt advice
Explore the skill
Agentic Loop
Loop invariants, retries, budgets, and stopping rules
Tools and Permissions
Typed tools, risk classes, and approval flows
Context and Memory
Retrieval, compaction, and durable state
Security and Evals
Guardrails, tracing, evals, and launch gates