How to Build an AI Agent for Organizations in 2026: Architecture, QA, Deployment, & More

As modern AI models and LLMs become better at reasoning, calling tools, and following structured schema, AI agent development has accelerated. Unfortunately, this acceleration has brought us back to a familiar enterprise trap — it’s easy to ship a convincing demo but much harder to prove that the system behaves predictably across real traffic, real data, and real edge cases. OpenAI’s own guidance and documentation emphasize that careful design patterns and operational practices are crucial to “ensure your agents run safely, predictably, and effectively.”

This reliability and predictability concern is exactly where AI-native QA becomes crucial. Non-deterministic systems like AI agents require dynamic probing and repeatable scoring to truly evaluate behavior and safety at a broader scale. Today, we’re going to discuss how to responsibly build an AI agent for enterprises and organizations.

Key Takeaways:

• AI agents should be treated as software systems, not “smart chat.” You need to clearly define scope, mark explicit boundaries, and choose a deliberate architecture to ensure that your agent is predictable and safe across messy, real inputs.

• Testing has to shift from “golden prompts” to repeatable evaluation and statistical reliability. In other words, agent behavior needs scenario suites, adversarial cases, rubric-based grading, and success-rate tracking across input variations.

• Inflectra’s new SureWire platform provides AI-native QA that behaves like a test manager. It deploys bespoke testing agents that act as hostile hackers, as confused customers, and more to evaluate AI agents more thoroughly, bridging experimental GenAI with the enterprise-grade reliability that Inflectra is known for.

Why Create an AI Agent for Businesses?

In short, AI agents can help businesses in a more tangible way than chatbots and traditional LLMs, saving time, resources, and money to give your customers a competitive advantage. AI has likely been a major topic of discussion at almost every company over the last year or two, but adoption and real productivity gains have been slower than initially expected. However, agents are potentially changing the game — they go beyond simply answering questions, and can execute multi-step workflows across systems. This is the real, concrete productivity enhancements that were promised.

AI agents are already helping businesses reduce time-to-resolution for support, standardize how complex internal processes are performed, and take informed action across CRMs, product analytics, internal knowledge bases, and more. On top of this, agents are more customizable and can be trained on an organization’s specific data, needs, and brand voice much better than a generic LLM or AI tool. This provides a significant improvement in output quality via more focused and contextualized answers and capabilities.

When Should You Build an AI Agent?

We recommend considering an AI agent for jobs or tasks that meet these three criteria:

• Multi-step work that requires tool usage: Agent orchestration patterns like planner-executor, router, specialists, and tool loops offer the most value here.

• Benefits from bounded autonomy: Agents can take actions safely within a well-defined environment (both permissions and rollback paths).

• Measurable success is clearly explainable: These projects usually stall when they depend on subjective “seems fine” evaluation, so scoring and reports are crucial.

For example, enterprise tasks or scenarios that fit these criteria might include:

• Triage and resolution support: Agent interprets a case, retrieves account context, proposes steps, drafts customer-safe response, and routes engineering tickets with evidence.

• FinOps copilot: Agent investigates invoice anomalies, reconciles records, generates audit-ready narratives, and prepares actions for human approval and execution.

• Internal enablement: Agent acts as an “expert assistant” that retrieves and synthesizes policies and procedures, as well as launching compliant workflows via approved tools.

How do AI Agents Differ from Chatbots?

The biggest difference between agents and chatbots is what they can actually do — chatbots are optimized to provide a helpful answer, while agents are optimized to complete a task. In other words, an agent’s output is not just the text response, but also the correctness of tool selection, tool parameters, and the sequence of steps across a workflow. However, this also changes how testing operates because workflows are often deterministic (e.g. business rules, API constraints, permission checks, etc.), which contrasts AI agents’ non-deterministic reasoning and language behavior. This is why AI agents need to be evaluated more like self-contained software systems, which requires dynamic assessments (more on this later).

How to Build an AI Agent in 10 Steps

AI Agent Protocols

Before we start building an AI agent, it’s important to understand the different protocols that can be used. These help agents reliably connect, act, and interoperate, meaning that they form the structure of your build. Common protocols to be aware of as you’re designing and planning your agent include:

• Tool/function calling protocols: These structured actions define how a model requests an action with structured arguments, how your app executes it, and how results are returned.

• Connector protocols (e.g. MCP): Model Context Protocol and other similar formats standardize how LLM apps connect to external tools and data sources, replacing one-off integrations with a consistent “interface.”

• Agent-to-agent (A2A) protocols: As organizations move towards multiple agents and handoffs, A2A forms the interoperability layer so different agents can communicate, exchange capabilities, and coordinate actions across the enterprise.

• Safety and governance protocols (e.g. Amazon Bedrock Guardrails, NIST AI RMF): These are not as formalized as the others, focusing instead on operational standards like guardrails, policy enforcement, risk governance, and auditability.

Step-by-Step Guide

Now that we understand the building blocks (protocols) that make up AI agents, it’s time to start building one.

1. Define Your Purpose & Scope

The first step is to turn the idea for your agent into a job description — write a list of inputs, outputs, definition of done, explicit stop conditions, etc. Then, add your boundaries, such as accessible tools, allowed data domains, and escalation paths. In enterprise systems, these boundaries can commonly be the difference between a safe assistant and “excessive agency,” which OWASP specifically highlights as a key risk category.

Tip: We recommend writing down failure modes (e.g. unsafe actions, wrong tools, wrong data exposure, confident wrong answers, etc.) and designing the architecture so those failures become detectable and containable.

2. Choose Your Platform/Model & Architecture

If protocols are the building blocks or materials of your “home,” the platform, model, and architecture can be considered the actual foundation that they’re built on. This choice is often influenced by latency, cost, tool support, context needs, data residency, and availability in your cloud stack. When it comes to architecture, most agents start as a single agent with a tool loop, then evolve into orchestrated patterns (e.g. router, specialists, planner-executor, etc.) as tool count and task diversity increase. Keep in mind that, according to Anthropic, simple and composable patterns outperform complexity-first designs.

Tip: For platforms that will need to operate in multi-agent environments, consider the various protocol choices and roles, such as MCP for tools and context, A2A for interoperability, etc. We recommend doing this early because retrofitting connectors and handoff standards later in the process can be painful and costly at larger scales.

3. Collect & Prepare Your Training Data

Next, it’s time to organize the input data. While most enterprise agents don’t need to be trained from scratch, it’s important to collect, refine, and organize instruction, knowledge, and behavioral assets. This includes system prompts, policies, tool usage guidance, structured sources, authoritative references, tool traces, labeled evaluation cases, and more. All of this will help tailor your new agent’s knowledge and behavior to you and your systems, enhancing the output quality and enabling it to “learn” and improve over time.

Tip: We recommend leaning on context engineering for the most impact here (e.g. what information you retrieve, how you format it, and what the agent can do with it) instead of trying to fine-tune after everything has been organized and input.

4. Design the Tool Layer

The tools and actions built into the agent will serve as your actuation layer and should be treated similarly to production APIs. This means strict schema, clear error contracts, idempotency, and traceable audit logs. This layer should also use structured outputs and tool calling so that your agent generates machine-validated arguments instead of free-form text (which can cause interoperability and integration problems).

Tip: We’ve found that permission design matters more than prompt design in enterprise environments, so start with read-only tools and then add write tools behind approvals (but maintain a default-deny access policy).

5. Build the Context Pipeline

After the tool layer, we can begin setting up memory, connectors, and RAG for accurate information retrieval. Think of this step as defining what the agent “knows” at runtime, with the goal being relevance, freshness, and containment. You want the agent to only retrieve what’s needed to prevent distractions and potential prompt injection surfaces. Your agent should also use vetted sources of truth like APIs and databases rather than static docs (when possible). Lastly, untrusted content should be tagged and separated so the agent can’t override instructions.

Tip: MCP is key for this part of your agent build because it standardizes how tools and data sources are exposed to agents, reducing integration sprawl and simplifying governance.

6. Implement State

This step refers to long-term memory, execution traces, and user preferences that persist across sessions. State management is also where enterprise agents start resembling enterprise software. Its short-term state includes task variables, intermediate tool outputs, and other similar information. The long-term memory includes user preferences and durable facts, stored with explicit rules about what is allowed to persist. Lastly, execution traces include full tool call logs, retrieved context references, and decision points so you can debug and audit the agent’s reasoning and recommendations.

Tip: Execution traces are particularly important because they allow you to reconstruct why an agent acted — without this, the agent can’t operate safely in a production environment.

7. Add Safety & Control Surfaces

For enterprise AI agents, safety isn’t a single filter — it needs to be a set of multiple control surfaces that sit across multiple layers. These surfaces prevent dangerous agent failures, such as prompt injection, insecure output handling, cost blowups, excessive agency (which we referenced earlier), and more. At the policy layer (what the agent can see and say), it’s important to block common abuse patterns like denied topics, word filters, prompt attacks, and hallucinations. At the tool execution layer (what the agent can do), enterprise risks can be managed via tool allowlists, schema validations, human approval gates, and rollback paths. Runtime controls map to failures that chatbots usually don’t deal with but agents may run into, such as runaway loops, tool retry storms, excessive reasoning chains, etc.

Tip: Leverage proactive risk mitigation that shifts teams from subjective “chat testing” to objective, report-driven scoring across the different layers. This will help you pinpoint what is actually causing issues and where they’re happening in the agent’s processes.

8. Test, Validate, Iterate, & Test Again

This step is what separates the systems that demo well from the systems that survive and thrive in production. Traditional tests like unit testing, integration testing, and regression testing are all important here, but do not cover everything. As we mentioned earlier, agents are non-deterministic and context-dependent, which makes traditional manual and static automated testing nearly obsolete for testing dynamic LLM behavior. Agent assessments will need to incorporate offline evals, online evals, workflow-level grading, and rubric-based grading. Agentic testing and validation should also include adversarial tactics like prompt injection via retrieved documents, system prompt leakage, excessive agency tests (tricking the agent into taking unauthorized actions), and tool misuse tests.

Tip: CI/CD is becoming core to testing AI agents because of the potential for drift over time. In other words, agents should be continuously tested instead of a single pre-launch event, always scanning for potential vulnerabilities and prioritizing improvements.

9. Deploy Your Agent

Next, it’s finally time to deploy your agent. However, this step is also where many teams accidentally turn an agent into a liability, so it needs to be planned and executed thoughtfully. Your deployment plan should assume that:

• Capabilities will expand over time

• Mistakes will happen

• You need fast containment when that happens

This means including a killswitch or way to quickly disable the entire agent, creating an incident playbook (what to do when it fails), and organizing a risk register.

Tip: We recommend following NIST’s AI RMF, which emphasizes post-deployment risk management and continuous improvement.

10. Monitor & Continue to Refine

After launch, agents tend to “drift” because the world around them changes (and so must they). New products, new policies, new data, new user behavior, model upgrades, API changes, and more all influence the slow shifts in behavior and knowledge over time. Because of this, you will need to treat monitoring as both operational health and quality health. This includes observability of operational signals like latency, token usage, error rates, tool failure rates, retry counts, and cost per successful task (not just cost per request). It also includes quality monitoring of behavioral signals like task success rate, grounding rate, policy violation attempts, hallucination rate proxies (e.g. claims without sources or incorrect tool inputs), and escalation rate to humans.

Tip: Make sure that you don’t just monitor performance, but have a plan in place when things go wrong that encompasses automated containment actions, incident classification, and post-incident reviews that produce new eval cases and new controls. As put forward by NIST, monitoring and post-deployment management are part of the lifecycle, not an add-on.

Common Failure Modes of AI Agents

When it comes to enterprise agent deployments, the most common failures we see include:

• Context poisoning or prompt injection: Untrusted content in the documents being retrieved ends up overriding system intent (this is one of the top risks of AI agents).

• Hallucinated claims: Confident statements without evidence, which can lead to contractual risks, compliance risks, or bad customer outcomes in enterprise settings.

• Brittle tool calls: Choosing the wrong tools, wrong parameters, or failing to recover upon error due to weak application-side handling.

• Infinite loops or runaway retries: Repeated tool calls that result from poor stop conditions or missing timeouts.

Less Common Guardrails to Consider

We’ve talked a lot about how many new threat vectors AI agents have the potential to introduce and why testing them has evolved. We’ve also covered many of the more common risks like prompt injection, context poisoning, and hallucination. However, there are a few additional areas that you’ll want to consider setting up guardrails around:

• Insecure output handling: Treating model outputs as executable instructions without human validation can quickly turn into code execution, data corruption, and even privilege abuse.

• System prompt leakage or sensitive disclosure: You should assume that attackers and bad actors will try to extract hidden instructions, internal schema, or other backend information via crafted inputs that aren’t normal user inputs.

• Excessive agency: If the agent has too much power (e.g. write access and broad connectors) and not enough restrictions or gating, it may start performing tasks that you hadn’t expected and don’t have a plan in place to mitigate.

How to Secure Your AI Agent

We recommend using five layers when securing your enterprise agent:

• Identity and access: Least-privilege credentials per tool, short-lived tokens, and strict allowlists.

• Input and output defenses: Prompt injection detection and containment (especially for MCP-connected sources), output validation, and structured schema for action arguments.

• Tool execution controls: Application-side verification before executing any tool call, such as parameter validation, policy checks, and approval workflows.

• Data protection and privacy: Redaction, data minimization, logging hygiene, and clear boundaries on what can persist as memory.

• Continuous assessment: Ongoing audits and monitoring that is aligned to risk frameworks like NIST.

Why Traditional Testing Misses Agent Failures

As we’ve discussed, traditional testing will not cut it for these newer agentic failures. The primary reason for this goes back to deterministic vs. non-deterministic. Traditional testing assumes determinism, where the same input results in the exact same output. However, agents use probabilistic reasoning, their behavior changes with context, and the data environments they access are dynamic. Because of this, manual and static automated testing (as well as pass/fail testing) are essentially obsolete for LLM workflows and agentic testing.

This is the next frontier of software QA, and is where Inflectra’s new SureWire platform comes in to provide repeatable metrics, consistency scoring, and audit-ready proof of what was tested for AI agents. These are adapted to the AI age so enterprises and their vendors can deploy more reliable, safe, and effective agents with less risk.

What Agentic Testing Actually Looks Like

Although this frontier is still evolving, agentic testing needs to treat QA as a dynamic system. This includes:

• Specialized test agents that generate scenarios (including adversarial behavior)

• Orchestrators that run many variants across realistic conditions (such as different user phrasing, different doc states, and different tool failures)

• Judge and evaluator agents that score outputs against rubrics (like policy compliance, tool correctness, UX resilience, etc.)

• A system that clusters failures and creates actionable remediation guidance

This is how SureWire approaches agentic testing — using vetted and specialized agents to test new agents under development. It simulates hostile hackers, confused customers, and expert evaluators to produce thorough assessment reports with strengths, weaknesses, and even prioritized recommendations for improvement.

Practical AI Agent Build Checklist

To recap everything we’ve covered today, here’s a quick checklist for building your AI agent:

• Define job, boundaries, and stop conditions

• Pick architecture pattern, but start simple (add orchestration only when needed)

• Implement tool layer with strict schema and app-side validation

• Build context pipeline with retrieval containment and connector governance

• Add task state, memory rules, and full execution traces

• Design safety with guardrails/policy checks, redaction, approvals, and cost ceilings

• Create evaluation suite for representative, adversarial, and tool correctness

• Run statistical reliability testing (repeat runs, success rates, failure clustering)

• Ship with observability and rollout controls like fallbacks, killswitches, etc.

• Make evaluation a CI/CD gate, monitor drift, and continuously retest

Looking for Tools to Build AI Agents? SureWire Helps Ensure Their Quality & Security

Inflectra has always strived to empower developers and testers to create the safest and most effective software possible. This spans test management, test execution, project management, risk mitigation, and much more, but there has been a huge shift with AI agents. These new systems can’t be evaluated in the same way, especially in highly-regulated industries where sensitive data needs to be properly handled and not exposed to third-party vendors.

This is where SureWire comes in — we had to build a new QA platform from the ground up to handle the complexities and frameworks of AI agent development. SureWire has all of the capabilities and requirements we’ve discussed today, paired with Inflectra’s stellar compliance track record. As with our other Inflectra software, SureWire’s systems are compliant with the following global regulations and certifications, so you can rest assured that your data is always protected and secure — including in strictly-regulated industries like aerospace, healthcare, and finance:

Inflectra Global Regulations Compliance	Inflectra ISO/IEC Certifications
GDPR (General Data Protection Regulation) HIPAA (Health Insurance Portability and Accountability Act) GAMP (Good Automated Manufacturing Practice) DORA (Digital Operational Resilience Act) NIST (National Institute of Standards and Technology) Center of Excellence FMEA (Failure Mode and Effects Analysis) FDA 21 CFR Part 11 Eudralex Volume 4 Part I & II DO-178C (Airborne Software)	ISO 26262 ISO 13485 ISO 31000 ISO 20022 ISO 27001:2013 ISO 9001:2015 IEC 62304 (Cybersecurity for Industrial Automation and Control Systems) IEC 62443 (Medical Device Software)