Build High-Quality AI Agents and Manage Them with Rigorous Evaluations

At its simplest, an AI agent is a system that performs tasks on behalf of a user by combining a language model with connectors (data, APIs, actions) and a control loop. 

It’s more than a chatbot: an agent can retrieve documents, call services, make decisions, and take multi-step actions. If you want a quick answer to what an AI agent thinks of, think of it as “an autonomous assistant that chains reasoning, retrieval, and actions to complete goals.”

Knowing this helps when you plan how to build an AI agent: you must design not only the conversation, but also the retrieval, actions, monitoring, and safety controls.

If you want to learn how to build AI agents, follow a structured approach:

1. Start with a tight use case. Identify one clear task the agent will do (FAQ resolution, scheduling, triage). A narrow scope reduces the failure surface and accelerates learning. This is the most important step in building AI agents that deliver value quickly.
    
2. Design the action surface. Enumerate the APIs, databases, and tools the agent may call. Define permitted actions and a human-in-loop handoff for risky operations.
    
3. Choose the stack. For rapid prototyping, use no-code/low-code AI builders (Copilot Studio, Lovable.dev, or LangChain-based pipelines). For production, use a modular setup where model calls, retrieval, and action orchestration are separate.
    
4. Create prompt templates and retrieval chains. Build templates for task flows and a reliable retrieval mechanism (vector DB + embeddings) so the agent grounds its outputs. This is core to building AI agents that do not hallucinate.
    
5. Build a sandbox and test harness. Simulate user inputs, edge cases, and action failures without touching production systems. The session emphasized that running agents in a safe sandbox is essential to learning how to build an AI agent that behaves under real conditions.
    
6. Evaluate, iterate, deploy. Run controlled trials, collect metrics, and iterate before full rollout.
    

Repeat these steps, and you’ll have a predictable path from idea to working agent.

Knowing types of AI agents with examples helps you pick the right design:

• Task agents (single-purpose): Example: an agent that schedules meetings. These are the easiest to build and safest to deploy.
   
• Info agents (retrieval-first): Example: a knowledge assistant that answers product questions by retrieving docs. They rely on strong retrieval.
   
• Workflow agents (actionable): Example: an agent that files tickets, updates CRM records, or initiates refunds. These require robust action controls and audit trails.
   
• Autonomous AI agents: Example: agents that manage multi-step processes end-to-end, such as buying ad inventory or running test suites. These need the strictest governance and observability.
   

When planning to build AI agents, start with task or info agents and only move to autonomous AI agents after you have solid evaluation and monitoring in place.

Evaluating agents: AI agent evaluation that matters

AI agent evaluation is not a single metric. The webinar highlighted a layered evaluation strategy:

• Functional metrics: success rate for tasks, completion time, and action accuracy.
   
• Quality metrics: relevance, factual accuracy, and hallucination rate for generated content.
   
• Safety metrics: false positive rate for sensitive actions, compliance checks, and policy violations.
   
• User experience metrics: user satisfaction, number of clarification turns, and abandonment rate.
   
• Operational metrics: latency, error rate, and resource cost per task.
   

Design tests that mirror real user journeys and capture these metrics. Good AI agent evaluation ties back to business outcomes: reduced response time, fewer escalations, or higher task throughput.

Observability and tooling: AI observability tools you need

Monitoring an agent requires observability at multiple levels. Use AI observability tools to:

• Log prompts, model responses, retrieval contexts, and decision traces.
   
• Surface anomalies with automated alerting when success rates drop or hallucination spikes.
   
• Replay sessions in a sandbox to reproduce failures.
   
• Track downstream effects of agent actions (e.g., did the ticket created actually resolve the issue?).

Invest in telemetry and dashboards that correlate model behavior with business KPIs. The session stressed that without good AI observability tools, teams cannot detect drift or diagnose why an agent’s performance changed after a model or data update.

Autonomous behaviors are attractive, but they increase risk. Best practices:

• Permission gates: require human approval for high-risk actions.
   
• Confidence thresholds: only let actions proceed automatically when the model’s confidence and retrieval relevance exceed a set threshold.
   
• Explainability: store the rationale and sources used for any decision the agent makes.
   
• Rollback and audit: every action should be reversible or auditably logged.

These controls are non-negotiable for autonomous AI agents that affect customers or finances.

Tools and platforms for building AI agents

There are many AI builders and AI building software choices. The practical advice from the webinar:

• Use LangChain or similar frameworks for custom pipelines where retrieval and logic matter.
   
• Use no-code AI builders for prototypes to validate use cases quickly.
   
• Select a vector DB (Pinecone, Weaviate) for retrieval-heavy agents.
   
• Pair models with retrieval and tool-execution layers to reduce hallucination risk.

Experiment with AI builders for early testing, then transition to robust AI building software for production.

Common pitfalls in building AI agents

• Scope creep. Trying to solve too many tasks at once breaks reliability.
   
• Poor retrieval. Agents that lack a strong grounding hallucinate.
   
• No observability. Lack of logs makes debugging impossible.
   
• Missing escalation. Agents taking irreversible actions without human oversight is a major risk.

Avoid these by keeping builds incremental and evaluation-driven.

Consider this checklist your launch gate: it ensures that the agent is up to safety, performance, and accountability standards. 

Test assumptions, record evidence, and pilot, pause, or roll back with it. Test the items in sandbox tests and rerun in a little production pilot, and record results to trace fixes.

Strive to reach a measurable preparation, rather than perfection - tried reliability in actual use is the goal.

1. Build a sandboxed prototype using AI builders.
    
2. Implement retrieval with a vector DB and prompt templates.
    
3. Add explicit action permissions and human-in-loop gates.
    
4. Instrument logs for prompts, retrieval context, model output, actions, and outcomes.
    
5. Run AI agent evaluation across functional, quality, safety, and UX metrics.
    
6. Pilot with a small user group, collect feedback, iterate.
    
7. Deploy gradually with monitoring and rollback options.

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Developing AI agents is a collaboration of product design, software engineering, and responsible AI practice. 

Want to learn how to build AI agents to simply automate faq responses or how to operate autonomous AI agents in complex workflows? 

The same path of starting small, heavily instrumented with AI observability tools, and using thorough AI agent evaluation to make decisions applies. 

One thing that was made clear during the webinar was that rigorous evaluation is not a choice. It is the secret of scaling the agent deployments.