Agentic AI for Production: What Engineering Teams Need to Get Right

AI agents are moving from experimental projects into product roadmaps and operating models. Deloitte reports that 74% of organizations expect to deploy agentic AI within the next two years, while only 21% have a mature governance model for autonomous agents. Deloitte’s research highlights the implementation work that remains as organizations move toward broader adoption.

Engineering teams are responsible for much of that implementation work. They need to define where an agent fits in a product workflow, connect it to the appropriate data and systems, control the actions it can take, evaluate its behavior, and support it after release. 

The following steps provide a practical framework for moving an agentic idea from an initial use case to a reliable production capability. They start with the workflow the agent should support, then address the data, tools, autonomy boundaries, testing, operational visibility, and recovery paths required to run it safely.

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Design the workflow before choosing the technology

Agentic AI is most useful when it is applied to a specific workflow with a defined objective.

Teams should start by identifying work that involves gathering information from multiple sources, checking systems, applying repeatable rules, preparing work for review, or moving tasks between systems.

For example, an account-management platform could use an agent to prepare a renewal brief. The system might gather product-usage trends, upcoming contract dates, unresolved support tickets, and relevant account notes. It could identify missing information, summarize potential concerns, and prepare discussion points for the account owner.

A cybersecurity product could use an agent to collect alerts, logs, asset information, and relevant incident history before an analyst begins an investigation.

A software delivery platform could use an agent to organize pull requests, issue status, test results, and deployment data into a release-readiness summary.

These workflows are relatively well suited to agentic systems because they have identifiable inputs, known data sources, and outputs that a user can review. They also provide clear boundaries for engineering teams to define what the agent should—and should not—do.

Anthropic's guidance on building effective agents recommends starting with the simplest approach that can complete the task and adding complexity only when it is necessary.

Connect the agent to the right data and systems

An agent can only complete a workflow effectively if it has access to the information and tools that workflow requires.

Depending on the application, that may include:

• Product and usage data
• Customer records
• Internal documentation
• Databases
• APIs
• Retrieval systems
• Workflow platforms
• Business rules

However, access should be deliberately scoped. For a renewal workflow, an agent may need contract dates, usage information, account notes, and open support issues. It doesn’t necessarily need unrestricted access to every customer account, financial record, or internal document.

Engineering teams therefore need to determine:

• Which sources are authoritative
• How frequently information is updated
• How conflicting information is handled
• What happens when required data is missing
• Which users can access specific information
• Which tools the agent can invoke
• Which actions require additional approval

These decisions are application-engineering problems as much as AI problems. Permissions should be enforced through application and system controls rather than relying on model instructions alone.

A narrowly defined toolset also makes an agent easier to test and operate. An agent that can retrieve account information and prepare a follow-up task has a more manageable risk profile than one with broad permission to modify customer records.

Separate recommendations from actions

Not every agent needs to act autonomously. There’s an important difference between an agent that prepares a recommendation and one that executes the resulting action.

An agent might identify a customer who needs follow-up and draft a message for a salesperson to approve. A more autonomous implementation might send that message automatically.

Similarly, an agent might identify information that needs to be updated while conventional application logic controls whether the update can actually occur.

The appropriate level of autonomy depends on the consequences of an error. Higher-impact actions may require validation or human approval, including:

• Modifying customer records
• Sending external communications
• Changing access rights
• Processing payments
• Deploying code
• Triggering operational workflows

Engineering teams should define these boundaries explicitly rather than treating autonomy as an objective in itself. The goal is to build a workflow that is reliable and useful—not simply to maximize the number of actions an agent can perform without human intervention.

Evaluate agent behavior before production

An agent can produce a convincing final result while making an important mistake along the way.

It might retrieve the wrong record, select an inappropriate tool, overlook a policy exception, or attempt an action outside the user's permission scope. That means evaluating the final output alone is not enough.

Engineering teams should test both what the agent produces and how it behaves while producing it.

A useful evaluation process should determine whether the agent:

• Retrieves relevant, approved information
• Uses the appropriate tools
• Respects user, tenant, and role-based permissions
• Applies business rules consistently
• Handles missing or conflicting information
• Requests clarification when necessary
• Escalates when it reaches a defined boundary
• Avoids actions outside its intended scope
• Completes the workflow within acceptable cost and latency limits

Testing should use realistic workflow scenarios rather than only isolated prompts.

Anthropic's guidance on evaluating AI agents recommends defining measurable success criteria, using realistic tasks, and reviewing agent traces rather than relying exclusively on final outputs.

Production feedback should then become part of the evaluation process. User corrections, failed tool calls, unexpected edge cases, and workflow failures can reveal where the system needs better data, different instructions, stronger controls, or a redesigned workflow.

Build observability into the product

Traditional application monitoring is not enough when an application includes agentic behavior. Engineering teams need visibility into how the agent interacts with models, tools, data, and users.

Depending on the workflow, useful telemetry can include:

• Model calls
• Tool calls
• Retrieved information
• Workflow steps
• Errors
• Latency
• Token usage
• Cost
• Human approvals
• Escalations
• Failed tasks

This information makes it possible to investigate why an agent failed. For example, an incorrect result could originate from poor retrieval, an unavailable API, conflicting data, inappropriate tool selection, or an incorrect model response. Without tracing, those failure modes can be difficult to distinguish.

Observability also has a direct operational role. Agentic workflows may require multiple model calls and tool interactions to complete a task, so teams need to understand how those interactions affect latency and cost.

Design for failure and recovery

Agentic applications depend on multiple components, including models, APIs, databases, retrieval systems, and external inputs. Failures are therefore part of the system design problem.

Teams should determine how the workflow behaves when:

• Required information is missing
• Two systems provide conflicting information
• An API call fails
• A tool returns an unexpected result
• The model proposes an invalid action
• A user lacks the required permission
• The agent cannot confidently complete the task
• The workflow exceeds its latency or cost threshold

The correct response will vary. A transient API failure might justify a retry. Missing information might require clarification. An authorization issue may require escalation. A high-impact action may need explicit human approval.

In some cases, the right solution is to fall back to conventional application logic rather than asking the agent to solve every problem.

Production engineering means defining these paths before failures occur.

Use conventional software where it provides stronger guarantees

Agentic AI does not mean replacing deterministic application logic with an LLM. In many production systems, the strongest architecture will combine model-driven behavior with conventional software controls.

An agent might gather information, interpret unstructured content, and prepare a recommendation, while application logic handles:

• Authentication
• Authorization
• Data validation
• Financial limits
• Required fields
• Approval requirements
• Business rules
• Deployment safeguards

This division can make the overall system easier to test, secure, and operate. The engineering question should therefore be: where does agentic behavior create useful flexibility, and where does deterministic software provide a stronger guarantee?

What experience do engineers need?

Agentic product development brings together several areas of engineering. Depending on the workflow, teams may need application engineers, AI engineers, data specialists, security practitioners, platform engineers, and product or domain experts.

The required combination will vary. An agent that prepares an internal summary for human review has different engineering requirements from one that retrieves information across multiple systems and initiates approved actions.

Across both scenarios, however, production experience matters more than familiarity with a particular model or framework.

Engineers working on agentic systems should be able to demonstrate experience with:

• Defining an agent around a specific workflow
• Connecting models to APIs, databases, retrieval systems, or tools
• Implementing authentication and authorization
• Designing approval and escalation mechanisms
• Handling incomplete information and tool failures
• Building evaluation cases
• Monitoring production behavior
• Tracing tool calls, errors, latency, token usage, and cost
• Working with product, data, security, and domain stakeholders
• Using conventional application logic for deterministic or high-impact decisions

The tools a candidate has used still matter. But the more useful discussion is usually about the engineering decisions they made and the production outcomes they supported.

Models, providers, and frameworks will continue to change. Experience building secure, observable, and maintainable systems is more transferable.

How to vet engineers for agentic AI experience

Familiarity with an AI framework or experience building a chatbot doesn’t necessarily demonstrate production agentic engineering experience. When evaluating engineers, ask them to explain specific systems they have built and the decisions they made.

Useful questions include:

• What workflow did the agent support?
• How did you define success?
• What data sources and tools did it use?
• How did you enforce user and role-based permissions?
• Which actions could the agent take directly?
• Which actions required human approval?
• How did the system handle missing or conflicting information?
• What evaluation cases did you create before release?
• How did you monitor the system after launch?
• What production failure did you encounter?
• How did you diagnose and fix it?
• Where did you use conventional application logic instead of model-driven behavior?

Strong candidates should be able to explain not only what their system did, but why it was designed that way. They should also be able to discuss limitations, failure modes, and the boundaries of the agent's autonomy.

Build around the product workflow

The engineering challenge of agentic AI is not simply connecting an application to a model.

Production systems require teams to think through the entire workflow: what the agent is responsible for, what information it can access, which tools it can use, what actions require approval, how its behavior will be evaluated, and how the system will be monitored after release.

For engineering leaders, those requirements provide a practical way to assess the capabilities needed for an agentic product initiative.

Start with the workflow on the product roadmap. Define the data and tools it requires. Establish boundaries around access and actions. Build evaluation and observability into the system. Then identify the engineering experience required to build and operate it.

FullStack helps companies find and vet engineers with hands-on AI and agentic development experience.

Book a meeting with our team to discuss the workflow on your roadmap today.