Speed to deploy artificial intelligence (AI) agents and speed to return are not the same thing. A 2026 Salesforce survey of more than 2,000 leaders deploying agentic AI found that companies running agents in production reach meaningful return on investment (ROI) in about eight months on average. Professional and business services, among the slowest sectors to adopt agents, got there in about 6.5 months. High tech, one of the biggest deployers, took about 10. Among the factors associated with success were clean, accessible data, narrowly defined agent scope, and human escalation paths established before deployment.
Long-term success depends on getting two stages right. The “first mile” requires organizations to define the business problem, workflow, success metrics, data requirements, and appropriate level of autonomy before an agent goes live. The “last mile” focuses on what happens afterward: adoption, monitoring, feedback, governance, and continuous improvement.
The First Mile Starts with the Business Problem
The pressure to experiment with agentic AI is growing quickly. Deloitte’s 2026 State of AI in the Enterprise found that close to three-quarters of companies plan to deploy agentic AI within two years, yet only 21% report having a mature governance model for autonomous agents.
That gap matters because autonomy raises the stakes. A tool that summarizes a document creates a different risk profile from an agent that can access enterprise systems, modify records, communicate with customers, or initiate transactions.
Before choosing a platform or building an agent, organizations need to understand what they are asking the technology to accomplish. What business problems need to be solved? Which systems and data will the agent need? What decisions can it make independently?
How will the organization know whether it is improving the process? Data readiness belongs in this step too: an agent can only act on information it can access and trust.
The workflow itself also needs attention. Adding an agent to an inefficient process does not automatically make that process effective. Businesses should first identify bottlenecks, understand where human judgment remains necessary, and determine where autonomy can create meaningful value.
Building Guardrails Before Agents Go Live
Unlike traditional software that follows predefined instructions, agents can interpret information, make decisions, use tools, and take actions across multiple systems. Organizations therefore need visibility into more than whether the technology is running. They need to understand what an agent did, what information influenced its decision, and whether the action remained within established policies.
Gartner predicts that by 2029, at least 70% of organizations with production agentic AI in infrastructure and operations will experience a material service, security, or cost incident linked partly to insufficient runtime controls.
Observability, traceability, benchmarking, and guardrails should therefore be built into the architecture from the beginning. Several steps can help:
- Define the autonomy boundary: Determine what an agent can do independently, what requires human approval, and what should remain restricted.
- Establish a baseline: Measure the existing workflow so improvements can be compared against current performance.
- Build visibility into the system: Track actions, tool usage, exceptions, costs, and escalation patterns from the start.
- Plan for exceptions: Define what happens when an agent is uncertain or encounters a situation outside its intended scope.
The Last Mile Begins in Production
Moving an agent into production introduces variables that are difficult to reproduce during testing. Employees may use it differently than expected. Data changes. Workflows evolve. New edge cases emerge.
As a result, agentic AI should not be treated as a one-time implementation. Organizations need feedback loops that combine technical performance with employee experience. Users should have a simple way to flag poor results, unexpected behavior, or unnecessary friction. Those signals can help determine whether an agent needs different instructions, stronger controls, additional data, or changes to the workflow itself. This is the thinking behind how we co-develop agents with clients: engineers are embedded with the client teams, so what users flag in production goes back into the build cycle directly.
Adoption is equally important. Employees need to understand not only how to use an agent, but also when to rely on it, what they remain responsible for, and when human judgment should take over.
The test of the last mile is whether the solution still supports the outcome it was built to improve, months after production launch.
Measuring Agentic AI Beyond Task Completion
Productivity, task completion, and cost savings remain useful metrics, but they do not tell the whole story. An agent may complete tasks quickly while creating additional review work. Another may reduce processing time but generate too many exceptions. Businesses should evaluate agentic AI against a broader set of measures:
- Accuracy and quality: Is the agent producing reliable results?
- Human intervention and exceptions: How often does a person need to step in, and where does the agent struggle?
- Adoption: Are employees incorporating it into their work?
- Cost per verified business outcome: Does the value justify the resources required?
- Business impact: Is the agent improving the outcome it was deployed to address?
These measures provide a clearer picture of whether an agent is delivering sustainable value rather than simply generating activity.
The Next Phase of Agentic AI
The first and last miles are closely connected. Decisions about the business problem, autonomy, workflows, metrics, and controls made before deployment determine what an organization can measure and improve afterward. What businesses learn throughout the effort should then feed back into the agent build and the workflow around it.
The organizations that see the best returns from agentic AI tend to be the ones that start with a well-defined business problem, set the controls before go-live, and keep improving the system after launch.
