Updated: July 8, 2026

As enterprises move AI agents into production workflows, building agentic systems that deliver reliable business value is becoming a priority for corporate leaders. In fact, Microsoft’s 2025 Work Trend Index found that 81% of leaders expected AI agents to be moderately or extensively integrated into their company’s AI strategy within 12 to 18 months.

Still, while these systems can support complex workflow automation, deploying them in production environments often presents practical challenges around reliability, security, and accuracy.

Teams therefore need a structured approach to agent design, governance, and deployment to ensure they can support real workflows without creating unmanaged risk.

In this guide, we explain what AI agents are, outline a practical enterprise framework for building them, and explore some of the most common implementation challenges.

What is an AI agent?

An AI agent is an LLM-powered software system designed to pursue a defined goal through a sequence of tasks.

Unlike basic chatbots that respond to isolated prompts, agents can move work forward across a workflow, using context, approved tools, and intermediate results to determine what happens next.

An agent may be accessed through a conversational interface or triggered automatically by an event in a business system. In either case, what makes it agentic is not the interface, but its ability to progress through a defined task or workflow instead of simply generating a one-off response.

For example, a sales lead-generation agent might ingest a new inbound lead form, query approved internal or external sources to enrich the company profile, evaluate the results against ideal customer criteria, and draft a personalized outreach email for a salesperson to review before it is sent.

AI agents do not need to be fully autonomous to be useful. In enterprise contexts, many agents are assistive or approval-based, especially when workflows involve customer communications, sensitive data, compliance risk, or actions in business-critical systems.

How to build an AI agent: A practical enterprise framework

AI agents can handle more variable inputs and workflows than traditional automation, but enterprise deployments still need clear goals and boundaries to keep agent behavior reliable and controlled.

1. Confirm the workflow is right for an AI agent

Before building an agent, teams should identify the specific workflow they want to support and confirm it actually requires agentic capabilities. To evaluate this, they should answer three main questions:

Is this the right kind of workflow for an agent?
Is there a clear business reason to build one?
Have the right stakeholders (such as workflow owners, IT, and end users) been consulted to validate that fit?

Agents are most useful when a task is repeatable enough to define, but variable enough to benefit from reasoning, context, and tool use. Good candidate workflows often combine multiple steps, unstructured information, separate business systems, and the need to interpret context as inputs or conditions change.

For example, a customer support triage agent may need to ingest an unstructured complaint email, retrieve account history from a CRM, cross-reference billing logs or relevant policy documentation, prepare a recommended resolution based on that specific customer's account tier, and route the case to the appropriate team for review.

In contrast, a traditional rule-based automation is often better for tasks with fixed inputs, predictable logic, stable decision rules, and clearly defined outputs, such as sending automated password reset links.

2. Define the agent’s role, scope, and autonomy level

Once teams have selected the workflow, they need to define what the AI agent is responsible for, where its responsibilities stop, and how independently it will operate.

Think of this step as writing a clear job description for the agent. Teams should establish who uses it, what specific outputs it should produce, what actions it can prepare or execute, and what remains outside its scope.

The appropriate level of autonomy should depend on the sensitivity of the workflow. For lower-risk workflows, teams may allow the agent to complete narrow actions directly, such as categorizing internal tickets or routing requests according to defined criteria. For higher-risk workflows, such as customer communications or updating important CRM fields, the agent may only prepare drafts or recommendations for human review.

Setting these boundaries early helps keep the agent aligned with the intended workflow while giving teams clarity on where human judgment is required.

3. Map the data, systems, and actions the agent needs

Once the agent’s role and scope are clear, teams need to identify the data, systems, and access requirements it needs to operate effectively.

This involves answering some key access questions:

What information does the agent need, and which business systems or data sources contain it?
Are those data sources approved, reliable, and properly permissioned for the workflow?
Does the agent only need to retrieve information, or does it also need the ability to prepare updates, recommend actions, or trigger changes in connected systems?
Which data, systems, or actions should remain off-limits?

Teams should identify the minimum level of access needed for the workflow, since giving an agent unnecessary access increases complexity, security risk, performance overhead, and the likelihood of unintended system behavior.

4. Choose the right model, deployment, and customization approach

Next, organizations should choose an AI foundation that can support the near-term agentic use cases they want to build, while fitting their broader AI strategy and operating environment.

Relevant considerations include model capabilities such as reasoning ability, context window, reliability, support for tool use and structured responses, and multilingual or multimodal support. Operational factors include latency, cost, security, and deployment control.

Organizations with an existing AI footprint should assess whether their current model, platform, retrieval setup, and access controls are sufficient to support those use cases. Those earlier in their AI adoption journey can use the requirements of priority workflows to guide model, platform, and vendor decisions from the start.

In many cases, an enterprise-grade LLM paired with clear workflow-specific instructions, retrieval from well-governed business data, and controlled access to the required tools is enough to support the agent’s core workflow. More specialized workflows may need additional tailoring, such as domain-specific instructions, tool configuration, or model fine-tuning.

Deployment decisions should reflect the organization’s risk tolerance, data sensitivity, control requirements, and expected agent use cases. While lower-risk internal workflows may be suitable for managed cloud platforms, sensitive agentic workflows may need more controlled deployment options, such as private cloud, VPC, or on-premises deployment.

5. Design orchestration logic and tool architecture

Agents need a clear operating structure that defines how work moves from input to outcome.

Orchestration logic is the set of rules and workflow paths that guide the agent through that process. It defines how the agent identifies a task, determines the next logical step, uses the right source or tool, carries relevant context forward, and reaches a clear endpoint.

For example, an invoice-processing agent might extract invoice details, check them against approved vendor records and purchase orders, flag discrepancies, prepare an approval summary, and route exceptions to finance for review.

Tool architecture covers the systems and capabilities the agent can use to complete those steps. Each connected tool should have a clearly defined role in the workflow, including when the agent should use it, what inputs it accepts, and what outputs it returns.

6. Set guardrails and evaluation criteria

Before real-world use, teams need to define the controls, approval points, and tests needed to determine whether the agent can operate reliably within the intended workflow.

Guardrails define the boundaries within which the agent operates. They enforce the agent’s predefined access and action limits, set the output standards it must meet, and specify the points where human review or approval is required. For example, low-risk outputs may only need retrospective spot checks, while sensitive, customer-facing, or business-critical actions may need human approval before anything is sent, changed, or executed.

Evaluation criteria give teams a concrete basis for deciding whether the agent is ready for controlled real-world use. These criteria can include whether the agent completes the task, produces accurate and complete outputs, grounds source-dependent responses in approved data, uses tools correctly, follows escalation rules, behaves consistently across similar scenarios, and meets latency expectations.

Agent activity should also be reviewable. Teams should be able to see what the agent did, which tools or sources it used, where it escalated, and which outputs were approved or rejected by human reviewers.

7. Deploy, monitor, and improve in real-world use

Once predeployment checks are complete, teams can move the agent into a controlled real-world rollout. At this stage, the goal is to monitor the agent’s live behavior, identify operational issues, and use performance data to decide whether to expand its use or automate additional workflow steps.

The rollout should match the risk and complexity of the workflow. For more sensitive use cases, a phased rollout may be the better option. This could mean starting with a smaller group, routing only a subset of eligible work through the agent, or applying stricter review rules until its performance is better understood.

In addition to monitoring output quality, teams should track the agent’s operational behavior, including user overrides, latency issues, tool failures, resource bottlenecks, and token usage. These signals can also show where instructions, tool definitions, access, or review rules need to be adjusted.

Common challenges when building AI agents and how to address them

Building reliable AI agents requires more than a sound implementation plan. Teams also need to anticipate the practical issues that can affect reliability in real-world workflows.

Tool integrations can introduce access and reliability challenges

Tool-use issues can arise when an agent has ambiguous instructions for when or how to use a tool, gets partial or unexpected results, lacks permission to complete a given step, or depends on an API that is slow or unavailable. These issues can then ripple through the workflow as failed lookups, delays, incomplete responses, incorrect record updates, or inappropriate data access.

To reduce these risks without adding unnecessary complexity, teams should validate tool use under realistic workflow conditions rather than only checking whether each integration works in isolation. The aim is to confirm that the agent selects the right tool, passes the right inputs, handles failed, incomplete, or unexpected responses appropriately, and stays within approved access boundaries.

Agents can lose reliability across multi-step workflows

In multi-step agentic workflows, a flawed early interpretation can shape later recommendations, routing decisions, approval summaries, or final outputs, making the agent harder to trust in operational settings. This can happen when an agent misapplies context, skips a required step, repeats work, or carries an unresolved error into later stages of the workflow.

Teams can reduce the risk by defining clear workflow paths, validating key intermediate outputs before they shape later steps, and routing failed, uncertain, or higher-risk cases to fallback paths or human review.

Inaccurate or ungrounded outputs can create downstream risks

An agent can produce outputs that sound plausible but are not supported by approved enterprise data. It may hallucinate details, misinterpret retrieved content, overstate what a source says, or omit context needed for users to understand the answer.

This is especially consequential when employees rely on the agent’s output to communicate with customers, assess cases, or make operational decisions.

Organizations can reduce these risks by grounding source-dependent outputs in approved information, validating high-impact outputs before they are acted on, and improving accuracy controls based on recurring output issues.

Security and governance can become more complex as autonomy increases

As agents gain access to more data, tools, and business systems, security and governance become harder to manage. Overbroad access, handling sensitive data without appropriate controls, unclear ownership, inconsistent deployment practices, or insufficient oversight can create compliance, accountability, and trust risks.

Organizations can manage these risks by applying least-privilege access, assigning clear ownership for each agent and workflow, and maintaining audit trails of agent activity. For sensitive use cases, organizations should also involve security and compliance teams from the start, require approval for higher-risk actions, and use more controlled deployment setups where needed.

Performance can degrade as usage scales

Agentic workflows may require multiple model calls, retrieval steps, tool interactions, or retries to complete a single task. As usage increases, that per-task complexity can compound, adding latency, increasing overall inference costs, exposing rate-limit issues, and making API or tool failures more frequent than they were during low-volume testing.

Performance problems can delay workflows, frustrate users, and make broader deployment harder to justify. Teams can reduce the risk by testing expected usage patterns before wider rollout, monitoring performance, reliability, and cost metrics, simplifying workflows where possible, and choosing models and deployment setups that fit the organization’s performance requirements.

Build enterprise AI agents with Cohere

AI agents can help teams complete more work faster by automating context-dependent, multi-step tasks within broader business workflows.

But since agents can access enterprise data, take actions across tools, and influence downstream business processes, building them is both an operational challenge and a technical one.

Organizations need to be deliberate about where autonomy adds value, where human judgment remains necessary, and how agent behavior will be tested and controlled in production. They also need an AI foundation that can support agentic workloads while maintaining enterprise-grade performance, efficiency, and deployment control.

Command A+ provides that foundation: a privately deployable mixture-of-experts (MoE) model built for high-performance agentic tasks that can run on as little as two H100 GPUs.

Request a Command A+ demo today.

How to build an AI agent: A practical guide for enterprises