It’s never been easier to spin up an AI agent. It’s also never been easier to end up with something flaky, unscalable, or flat-out unsafe.
In this guide, we’re sharing the design principles that have made the biggest impact in real-world builds. Whether you’re building your first agent or scaling a fleet of them, these best practices will help you avoid the common traps and design agents that actually work.
| Category | Data Governance | Information Governance |
|---|---|---|
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Scope
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Data governance focuses on technical data management for structured and unstructured data
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Encompasses all information assets, including data, documents, records, and communications. Ultimately, overseeing how data is used and protected
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Goals
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Data governance emphasizes data quality and usability
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Information governance prioritizes compliance and risk management.
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Stakeholders
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Involves IT teams, data engineers, and analysts
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Involves legal teams, compliance officers, executive leadership, and risk management teams
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Policies and Procedures
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Defines data collection, storage, access, and disposal standards
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Establishes enterprise-wide rules for document management, record retention, and ethical information use
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Impact
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Drives operational efficiency and data-driven decision-making
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Aligns information use with strategic objectives and regulatory obligations
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Examples of Implementation
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Centralized data catalog, defined data quality metrics, and clear ownership structures
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Legal defensibility for deleted records, enterprise retention schedules, and communication governance policies
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What is a workflow in n8n?
N8n defines it as: “a collection of nodes that automate a process. Workflows begin execution when a trigger condition occurs and execute sequentially to achieve complex tasks.”
TL;DR — The 10 AI Agent Design Best Practices to Copy Today
If you’re designing AI agents, don’t skip the fundamentals. These ten best practices will help you create agents that are more reliable, explainable, and aligned with user needs.- Draw clear boundaries – Separate agent decisions from tool execution and task scope. Use schemas and interfaces to define hand-offs.
- Plan, then act – Give agents structured reasoning loops so they think before they execute.
- Design the UX of autonomy – Treat agents like product surfaces with defined roles, behaviors, and escalation paths.
- Build in observability – Track every decision, tool call, and schema diff from day one.
- Keep evaluation in the loop – Combine golden tasks, canary tests, and human review to measure what matters.
- Control cost and performance – Prune prompts, parallelize subtasks, and escalate model size only when needed.
- Give your agent guardrails – Restrict access, teach fail-safe behavior, and protect against risky autonomy.
- Treat memory as part of UX – Design what gets remembered, how it gets used, and give users visibility and control.
- Iterate with real feedback – Use structured and unstructured signals to refine agent behavior continuously.
- Structure your team for agents – Align roles across product, UX, engineering, and ops to support responsible autonomy.
What is the difference between an AI agent and workflows?
It’s easy to get turned around with AI terminology. Especially considering the rate of development constantly creates new terms.
AI agents and workflows are often conflated, but are not interchangeable. And the distinction is important if you’re determined to master agent design.
A workflow follows a fixed sequence. It’s automated, yes, but always predictable.
An AI agent, on the other hand, has autonomy. It can reason, make decisions, and adapt based on context.
This matters because the architecture you choose affects how you test it, how safe it is, and how users experience it. Workflows are easy to evaluate with clear inputs and outputs. Agents require a different level of observability, guardrails, and UX design to manage the uncertainty that comes with autonomy.
A simple rule we follow here at Hatchworks AI is: Use workflows when the logic is clear and repeatable. Use agents when judgment, adaptation, or multi-step coordination is required.
If you’re interested in learning more about workflow automation, be sure to check out:
10 AI Agent Design Best Practices
Good agent design doesn’t happen by accident. It takes deliberate architecture, UX thinking, and iteration to build agents that are actually useful and safe. The practices below come from what’s worked (and what hasn’t) across real-world builds.
You don’t need to adopt all ten from day one. But the earlier you get these right, the fewer problems you’ll have to debug later.
#1 — Draw clear boundaries between agents, tools, and tasks
One of the easiest ways to create fragile agent systems is by blurring the lines between what the agent should decide, what a tool should execute, and what a task is meant to achieve.
You need to treat them as distinct layers:
- Agent = judgment and decision-making
- Tool = deterministic execution
- Task = a defined unit of work
If something is repeatable and has a known output, it’s a tool. If it requires interpretation or judgment, it stays inside the agent.
This separation makes systems easier to test, reason about, and debug when things go wrong.
What not to do: letting agents manage business rules
Say your agent is managing user access. If you describe access rules in the system prompt (“Only admins can delete users”), you’re setting yourself up for problems. The agent might misinterpret the rule, apply it inconsistently, or silently break when the prompt changes. You’ll also have a hard time tracing failures or validating behavior.
Business logic belongs in tools, not buried in freeform text. Tools should expose clear parameters like role=admin, action=delete, and reject invalid combinations by design.
What works better: defining interfaces and schemas
Instead of prose prompts, give your tools well-defined interfaces. A deletion tool, for example, might expect a payload like:
{
"userId": "abc123",
"requesterRole": "admin"
}
The tool validates that input before acting, and the agent simply decides when to call it, not how it works. You can test each piece in isolation, log schema changes, and avoid surprises when something downstream shifts.
#2 — Plan, then act with structured reasoning loops
Well-designed agents don’t guess their way through a problem. Instead, they plan, execute, and validate. This principle is about giving your agents a structure to follow, especially when they’re handling multi-step or ambiguous tasks.
Start with a plan: what needs to be done, in what order, and with what tools. Then execute against that plan. Finally, validate the result. Should the agent stop, retry, or escalate?
This loop (plan → act → reflect) gives agents direction and gives you better observability when things go off course.
What to do when the agent isn’t sure
Agents often have to act without perfect context. But sometimes, they don’t have enough to properly act in a way that end users will consider ‘successful’.
To account for these lapses, you can build confidence thresholds and fallback behavior. So, if the agent’s not sure what to do, it should be designed to pause and ask by prompting the user or logging a clarifying query.
We never want our agents to assume. We could tell you what assuming leads to, but that’s not very PG. So, instead, we’ll say: guessing leads to bad decisions and user distrust.
Make agent decisions explainable
Remember in math class when you would get points toward an answer on a test if you showed your work? Even if the answer was wrong, you could get partial credit.
Well, agents need to show their work too.
When they make a decision, they should explain why, but do so without exposing private prompts or irrelevant internals. Plain, user-facing language can help build user trust and make debugging easier when something goes wrong. It also helps stakeholders understand the system’s logic without needing to read a line of code.
#3 — Design the UX of autonomy
Autonomous behavior isn’t inherently useful. It’s useful when aligned with user intent. That’s the core UX challenge with agents. You need to give them enough freedom to be helpful without letting them go off the rails.
The traditional UX playbook assumes the system waits for user input. Agentic systems change that. They can observe, reason, and act independently. That means you’re designing behavior.
You need to treat the agent as a first-class product surface. That means defining its role, its scope, how it communicates, and how it escalates when unsure.
Choose the right interaction model based on context
Not every agent should act like a co-pilot. The way it behaves, and how users experience that behavior, should change depending on the use case. Here are four patterns we commonly use:
- Batch executor: Handles work in the background with no interaction (e.g., summarizing logs, flagging errors).
- Sidekick: Waits for instructions, then executes (e.g., a code linter or query generator).
- Co-pilot: Offers suggestions, explains reasoning, and asks for confirmation before acting (e.g., a design assistant).
- Overseer: Actively monitors for triggers and intervenes when necessary (e.g., a compliance agent flagging violations).
If your agent’s role isn’t clear, users will either over-rely on it or ignore it entirely. And if its behavior shifts without warning, trust drops off fast.
Don’t abandon core UX principles
The same principles that make any system usable still apply here:
- Clarity: Be explicit about what the agent is doing and why. Hidden logic erodes trust.
- User control: Let users pause, override, or undo agent actions. Autonomy doesn’t mean invisibility
- Feedback: Confirm actions, surface confidence levels, and explain decisions.
- Consistency: The agent’s tone and behavior should align with its role. A sidekick shouldn’t lecture.
- Accessibility: Ensure agent interactions are understandable, navigable, and inclusive.
- Ethics: Give users visibility into decisions that affect them, especially in high-stakes or sensitive contexts.
Well-designed agents are clear about what they’re doing, careful with when they act, and easy to interrupt when they get it wrong.
#4 — Build observability into your agent system from the start
If an agent makes a bad decision and you can’t explain why, you’re flying blind.
Observability has to be baked into the system from the start. That means tracking what the agent saw, what it did, and what happened next.
At a minimum, you should log:
- Every tool call, with full input/output payloads
- Retry attempts and error messages
- Diffs between expected and actual behavior
- Versioned prompts and schema changes over time
You also need tracing—the ability to replay an agent’s reasoning path for any given output. That includes its plan, intermediate steps, and final decision. If you can’t trace the behavior, you can’t improve it.
Build dashboards that surface operational health:
- Latency per task
- Token usage per request
- Tool success/failure rates
- Guardrail or policy triggers
Without this visibility, bugs go undetected, costs creep up, and trust breaks down. The more autonomy you give an agent, the more observability you owe your users and your team.
#5 — Evaluation in the loop
Agents often produce subjective, non-deterministic outputs (summaries, plans, validations) that require more than pass/fail assertions. That’s why evaluation needs to happen continuously, at multiple levels, and with humans in the loop where it matters.
Start with golden tasks: a curated set of real-world scenarios where the correct output is known. These give you a sanity check that the agent still behaves as expected, even as prompts, tools, or code changes.
Then add canary suites. These are small sets of tests that run before deployment to catch regressions early. They’re fast, lightweight, and ideal for surfacing high-impact failures before they go live.
Once in production, you’ll need two modes of evaluation:
- Offline evaluation, to analyze agent behavior in controlled conditions.
- Online evaluation, to observe how it performs with real users and unpredictable inputs.
For subjective outputs, build in human scoring loops. You will need to give reviewers structured rubrics and guidelines.
If your agent writes product descriptions or suggests design changes, you’ll need this layer to catch subtle errors that automated metrics miss.
Finally, tie evaluation to deployment gates where you set quality bars, define acceptable drift, and track regression budgets over time.
#6 — Control cost and performance through architectural discipline
Agent systems are expensive by default. Without clear limits and smart agentic architecture, you’ll burn through tokens, rack up latency, and strain your infrastructure long before you hit scale.
The solution is to be deliberate about how your agent works.
Start by giving your agents a token budget. You can define clear limits on how much context they can use per interaction, and monitor actual usage. What you don’t want to do is let prompts sprawl unchecked.
From there, focus on pruning and deduplication. Cut out irrelevant context, avoid repeating instructions, and trim the fat from system prompts because every unnecessary token you use adds cost and delay.
You also want to look for opportunities to parallelize. If your agent needs to process five inputs or validate multiple outputs, those independent subtasks don’t always need to run sequentially.
Caching is another easy win. If a tool call or model response is deterministic and doesn’t rely on fresh context, store the result and reuse it. This is especially useful for API-based tools or repetitive calls like entity lookups.
Not every decision needs GPT-4. For predictable steps, use smaller models or rule-based tools. Escalate to larger, more expensive models only for high-complexity tasks where reasoning is required.
#7 — Give your agent guardrails
Autonomous agents need boundaries. Without them, they’ll eventually make the wrong call. Sometimes in ways you didn’t think to test for.
That’s not theoretical. Anthropic’s recent research showed agents in simulated environments trying to manipulate users, even resorting to tactics like blackmail when their access or role was threatened. The lesson? If your agent can act, it needs clear rules around what it can do, when, and under what conditions.
Start with scoped access, where only the tools and data the agent truly needs can be read. Layer on rate limits to prevent runaway behavior. And if the agent is acting on sensitive data, make sure consent is part of the flow.
Something that’s just as important is teaching the agent when not to act. If context is missing or confidence is low, pause and escalate.
#8 — Treat memory as part of the UX
To do memory right, you need to distinguish between types:
- Short-term memory (scratchpads, intermediate results) helps an agent reason through multi-step tasks.
- Long-term memory (user profiles, past decisions) helps it personalize or carry context across sessions.
But memory introduces risks such as stale data, context overload, and unexpected behavior.
That’s why you need retrieval policies. These rules cover what gets remembered, when it gets refreshed, and how it gets used. Adding decay mechanisms helps avoid treating old data as always true.
You should also ensure transparency. Users should know what the agent remembers and have ways to reset or update that memory. Otherwise, personalization turns into unpredictability.
And don’t offload memory to your tools. Tools should stay stateless. It’s the agent who needs to decide what matters and when.
#9 — Iterate based on real feedback
Even well-tested agents behave differently under real use. Users phrase things unexpectedly, try edge cases, or just give feedback in the form of silence. That’s why you need to:
- Observe
- Collect
- Adjust
- Redeploy
One of our biggest tips is to capture structured and unstructured feedback. That way, you can gather quick thumbs-up/down responses, but you can also probe deeper with verbatim comments and usage patterns that reveal more about what’s working vs what isn’t. If someone rephrases a prompt three times, they’re telling you something your logs won’t.
Every update you make to your agentic system improves trust, performance, and alignment. And when you do make changes, make sure your users know about them. Publish changelogs, show what’s new, and make users feel heard.
Behind the scenes, treat prompts and policies like code where you version them, test them, and roll them back when needed.
#10 — Build the right team around your agent
Once you give software the ability to reason and act, you’re also introducing new types of responsibility. Who decides what the agent should do? Who defines how it interacts with users? Who steps in when it makes the wrong call?
Clear roles matter. You’ll need:
- Product shaping the problem and defining value
- UX owning consent, escalation, and recoverability
- AI engineers managing prompts, evals, and model behavior
- Backend teams building stable tools for the agent to call
- Site Reliability Engineering tracking how it all performs, and where it breaks
You’ll also need escalation paths. When the agent misfires, who reviews it? Who updates the logic? Who can stop it?
Don’t let ownership drift, and treat your team structure like part of the architecture. Because it is.
Going From Best Practices to Real Agentic Systems
Design principles are essential, but they don’t exist in isolation. Real AI agents are built by combining these practices into cohesive systems that can reason, adapt, and scale.
That’s where most teams hit friction.
You might nail decision boundaries or evaluation loops, but without the right platform, memory model, or UX layer, your agent won’t behave reliably. That’s why we recommend thinking in systems, not just features.
Best practices need to be composable. For example:
- Combine context management and guardrails to keep behavior predictable.
- Use planning loops alongside observability to track the decision-making process.
- Match your UX to platform capabilities to improve how users interact with the agent.
If you’re building agents that touch complex workflows or span multiple tools, aligning the technical and behavioral pieces becomes essential. Especially when agents start working alongside, or even coordinating with, other agents.
That’s where platform selection, system architecture, and task decomposition make or break your design.
Where to Build Your Agent: Choosing the Right Platform
One of the biggest decisions in designing AI agents isn’t how they reason, it’s where they run.
Do you build from scratch? Use an orchestration framework? Or layer on top of a workflow automation platform like n8n?
There’s no universal answer, but they are suited to different needs and teams.
Custom-built frameworks (like LangChain or Semantic Kernel) give you full control. You define the agent loop, manage memory and tool calls, and fine-tune every decision. That flexibility is great for specialized agents with complex workflows, but it comes with a steeper learning curve and more ongoing maintenance.
Visual platforms like n8n are ideal when you need fast iteration, clear visibility, and tight integration with external systems. They’re especially useful for:
- Connecting API calls and agent steps with minimal code
- Managing context across workflows
- Adding human-in-the-loop checkpoints
- Running different tasks in parallel with built-in error handling
And they’re increasingly agent-aware. Our guide to n8n AI agents walks you through your first build.
If you’re starting to choose platforms, consider:
- Does it support well-defined tasks with reusable components?
- Can it maintain context between steps or over time?
- Does it allow you to guide users and inspect behavior?
- What are the trade-offs between speed, control, and visibility?
When in doubt, start simple. Use something like n8n to validate your agent’s capabilities and expand from there.
Specialized vs. General-Purpose Agents: Choosing the Right Fit
Not every agent needs to do everything. In fact, trying to make a single AI agent handle too many complex tasks is one of the fastest ways to introduce instability, confusion, and bloated context windows.
That’s where the distinction between specialized agents and general-purpose agents becomes critical.
- Specialized agents are built for well-defined tasks. Their prompts, tools, and decision-making logic are all tightly scoped. This makes them easier to test, cheaper to run, and more reliable in production.
- General-purpose agents aim to handle a broader set of scenarios. They need stronger context management, better fallbacks, and more refined prompting strategies to handle ambiguity. These are your co-pilots, orchestrators, or customer support bots that span departments.
The trade-off? More power, more risk.
When you’re deciding which approach to take:
- Start with your user needs. Do they expect narrow automation or broad assistance?
- Look at the pain points you’re solving. Is it about precision or flexibility?
- Think about the decision-making process. How complex is it, and how explainable does it need to be?
In most systems, you’ll end up with a mix of general-purpose agents handling high-level intent and multiple specialized agents or tools executing discrete steps. That pattern scales well and keeps your architecture modular.
You might like: Understanding Agents and Multi Agent Systems for Better AI Solutions and Autonomous Agents: The Next Frontier in AI
Use Case Walkthrough: Breaking Down a PR with Agentic Systems
Let’s bring the best practices to life with a real-world scenario.
Say your engineering team struggles with huge pull requests. Reviewing them is slow, error-prone, and inconsistent. You want an agent that can automatically split large PRs into smaller, well-scoped changes where each is tied to a clear purpose.
At a glance, this feels like a simple automation task. But once you unpack it, you’re dealing with:
- Complex reasoning to interpret what the changes are doing
- Past interactions and commit history to understand intent
- Multiple steps, tools, and decisions that span different aspects of the codebase
A well-designed agent wouldn’t just slice based on file count. It would analyze differences, group related changes, and write human-friendly summaries for each chunk. It would know the expected outputs (e.g., isolated commits with descriptive messages) and escalate when it couldn’t confidently split something.
To make this work:
- You’d give the agent clear instructions on how to segment logic
- Add memory for past interactions and patterns
- Introduce schema-based tools to execute deterministic actions (e.g., create branch, label PR)
- Use feedback to continuously refine chunking strategy
The result is faster reviews, clearer commit history, and an agent that gets smarter over time.
This is the kind of real-world, evolving system where agent design shines. Not a static script, but an intelligent collaborator that supports better decisions, better collaboration, and better agent experiences for the team.
How to Identify AI Use Cases that Actually Deliver Value
Not every task is a good fit for an AI agent. The best use cases lie at the intersection of complex reasoning, fragmented workflows, and opportunities to reduce friction for end users.
Here’s a simple framework we use when evaluating whether something is worth building an agent for:
- Is judgment or context required?
If the task can be reduced to static logic, it’s probably better as a tool or automation. But if it requires interpreting nuance, making trade-offs, or adapting based on user behavior, it may need an agent.
- Does the task span multiple steps or systems?
Agents excel at coordinating different tasks across external systems, especially when there’s ambiguity or multiple ways to complete the work.
- Would the agent improve the experience over time?
If the task benefits from learning patterns, adapting to past interactions, or refining outputs based on feedback, an agent gives you a long-term edge.
- Are the outcomes hard to codify?
Tasks where “correct” outcomes vary based on tone, content, or stakeholder needs often require agents that can reason and make informed decisions.
The more of these boxes you tick, the stronger the case for agentic design. But don’t guess. Get structured about it.
Check out our guide on how to identify AI use cases for a deeper dive into common patterns, pitfalls, and a checklist you can use with your team.
Partner With HatchWorks AI to Ship Smarter Agent Systems
AI agents are changing how work gets done, but only when they’re designed with intention.
If you’re ready to go beyond prototypes and build agentic systems that are safe, observable, and actually helpful, HatchWorks AI can help. We combine deep technical expertise with UX strategy and real-world delivery experience to turn agent concepts into operational tools.
Whether you’re just starting to explore use cases or you’ve hit scale and need to rein in complexity, our team will help you build agent systems that adapt, integrate, and drive results.
