Best practices for building and managing agentic AI systems

Agentic AI systems are designed to operate independently—interpreting objectives, breaking them down into steps, and executing actions across tools and systems. This autonomy brings both opportunity and risk. If implemented carefully, agents can improve response times, reduce repetitive tasks, and unlock new efficiencies. But poorly scoped or loosely managed agents can behave unpredictably, create errors at scale, or introduce security gaps.

This article outlines practical design and operational guidelines for building reliable, safe, and effective agentic systems.

1. Start with well-defined task boundaries

Agents should operate within clear constraints. Without these, they may interpret vague goals too broadly or misuse available tools.

What to do:

• Define exactly what the agent is responsible for.
• List what tools, data, or APIs it’s allowed to access.
• Set limits on execution time, retries, and cost usage.

Why it matters: Unclear boundaries increase the chance of incorrect or unintended behavior. A narrowly scoped agent is easier to monitor and debug.

2. Use tool access controls and enforce safe execution

Most agents rely on tool use—whether APIs, scripts, or databases. These interactions must be secure and traceable.

Implementation tips:

• Build a tool registry: define each tool’s name, expected inputs, and allowed output types.
• Validate all parameters before passing them to external systems.
• Log every tool call with timestamps, input, and output.
• Use role-based access controls (RBAC) where possible.

Why it matters: An agent calling unsafe functions or interacting with critical infrastructure without constraints is a risk, not an asset.

3. Make the agent observable

You need to see what the agent is doing, why it's doing it, and what outcome it’s producing—especially when debugging.

Key elements to track:

• Steps the agent takes (task plan or flow)
• Decisions made (reasoning if available)
• Tool calls and responses
• Errors, retries, or fallbacks

Approach:

• Use structured logs rather than free-form text.
• Include task IDs to track execution chains.
• For long tasks, add status checkpoints.

Why it matters: Without transparency, it's hard to trust or improve agent behavior.

4. Add human approval where needed

Agents shouldn't act alone on everything. Insert approval steps for tasks that affect customers, revenue, or systems.

When to include human-in-the-loop:

• The task modifies user data, charges money, or sends public messages.
• There’s uncertainty in the agent’s reasoning.
• Legal, compliance, or brand tone checks are required.

Implementation examples:

• Approval workflows (e.g., manager must review before proceeding)
• Manual override options
• "Preview before send" for outbound messages

Why it matters: Even high-performing agents benefit from human guardrails in complex or sensitive scenarios.

5. Handle memory with care

Agents often use memory to carry context across sessions. But unmanaged memory can cause irrelevant context reuse or privacy risks.

Best practices:

• Separate temporary (short-term) and persistent (long-term) memory.
• Store only what’s needed for the task. Avoid “remembering everything.”
• Use retrieval methods like embeddings and semantic search to access relevant info.
• Periodically clean or prune memory stores.

Why it matters: Memory improves coherence—but only if it’s selective and accurate.

6. Structure how agents plan and replan tasks

Agents need to plan, execute, and sometimes change their plans. This should happen in a structured and inspectable way.

Suggested design:

• Use a task tree or dependency graph.
• For each task node: define objective, input, expected output, status.
• On error, support fallback plans or error-specific branches.

Why it matters: Structured planning improves predictability. It also helps when agents fail mid-task or encounter unexpected inputs.

7. Prevent loops, runaway behavior, and excessive retries

Autonomous systems can get stuck in loops if they aren’t designed to stop themselves.

Controls to set:

• Retry limits for failed tasks.
• Maximum depth for nested sub-tasks.
• Timeout settings for each phase of execution.
• Logging alerts for high repeat rates.

Why it matters: Agents should not keep retrying indefinitely or spiral into high-cost execution cycles.

8. Always validate output before use

Even when an agent completes a task, its output needs validation before it’s consumed by users or other systems.

Checklist examples:

• Is the output type (JSON, Markdown, text) correct?
• Are required fields present?
• Does the text contain hallucinations or out-of-scope content?
• If code or commands are generated, were they sandbox-tested?

Why it matters: Output that looks right at a glance might still be wrong or unsafe. Validations catch silent errors.

9. Collect feedback to improve performance

To evolve your agent over time, you need data on how it performs and where it struggles.

Ways to gather feedback:

• Collect user ratings on output quality.
• Track task success/failure rates over time.
• Log all escalations to human review.
• Add auto-metrics (e.g., latency, step counts, token usage).

Why it matters: This data helps fine-tune prompts, planning logic, and tool behavior.

10. Provide safe exits and fallback behavior

Agents must know how to fail gracefully. A stuck or looping agent that doesn’t return control is worse than one that exits cleanly.

Fallback strategies:

• Escalate the task to a human.
• Return a “cannot complete” message with logs.
• Revert partial changes (where applicable).
• Stop after defined thresholds.

Why it matters: A graceful exit avoids downstream damage and builds user trust.

Summary: 10 principles for reliable agentic AI

Closing thoughts

Building agentic AI is not just about giving models more freedom. It’s about designing systems that operate safely within that freedom.

The most effective agents are the ones that:

• Know what they’re allowed to do,
• Operate transparently,
• Fail predictably,
• Improve with feedback.

Start with narrow tasks, test extensively, and monitor everything. From there, you can expand their role and reliability over time.