During an interview, a candidate may be able to explain large language models (LLMs), write prompts, and build a working demo. But once a project involves procurement, legal, IT, and business users, a different set of questions emerges: Where should the data come from? Who makes the call when an exception occurs? Who must approve an amount that exceeds the authorized limit? How can the team trace an error after the fact? If a candidate cannot answer these questions, even a flawless technical demo does not prove that the system can be used in day-to-day operations.
To determine whether AI deployment talent can deliver in practice, companies do not need to ask yet another question about model knowledge. Instead, they should assess whether candidates can turn employees’ experience into consistent rules, break cross-functional work into executable tasks, and introduce human approval at high-risk stages. The team must also record the entire execution process so that managers, IT, and auditors can see what data the AI Agent used and what actions it took.
Knowing How to Write Prompts Does Not Mean Knowing How to Bring an AI Project into Daily Operations
TechOrange reports that Anthropic has invested US$100 million to establish the Claude Frontier Academy, with the goal of training 10,000 Frontier Deployed Engineers (FDEs) by the end of 2027. Companies first nominate senior software engineers with hands-on experience. After interviews with Anthropic, selected participants complete four days of in-person training and assessments before returning to their companies to carry out a 12-week real-world project. This training model emphasizes practical deployment capabilities rather than simply measuring how much participants understand in the classroom.
Anthropic believes that strong FDEs must be familiar with agentic AI systems while also possessing problem-solving skills, the ability to communicate and collaborate across departments, sound engineering capabilities, and curiosity. However, the report also notes that Anthropic is still developing a comprehensive assessment framework. It has said only that communication, problem-solving, and software development skills will be evaluated, without yet publishing a complete scoring methodology or criteria for measuring outcomes. Taiwanese companies can use this training process as a reference, but they must still define for themselves what level of performance qualifies a system for production use.
Consider procurement price comparisons. If a candidate can only have a model read three quotations and summarize the differences, they have not yet addressed the genuinely difficult parts. They must also clarify how exchange rates and payment terms should be compared, who should supply missing vendor information, where the process should stop when a price exceeds the authorized limit, and who is responsible for reviewing the conclusion before it is submitted. When evaluating talent, companies should ask candidates to explain how they have clarified requirements, handled exceptions, and built cross-functional consensus in the past. These experiences reveal practical capabilities far more effectively than certifications or prompt-writing portfolios.
Use a Live Cross-Functional Process to Assess a Candidate’s Ability to Deliver
Companies can use a currently operating process with a manageable scope as the assessment exercise, such as vendor document reviews, internal report consolidation, or customer service case classification. In addition to providing requirements documents, companies should allow candidates to interview the employees who actually manage the process. Documentation usually describes only standard operating procedures; real-world situations such as rejected submissions, requests for additional information, and unexpected exceptions often become apparent only through conversations with process owners. The assessment can focus on the following four areas:
- Data inventory: Can the candidate identify what data each step requires, where that data comes from, who is responsible for it, and how it is updated? If data is incomplete, the candidate should also explain whether the process should stop or be handed over to a person.
- Rule definition: Can the candidate turn the judgment of experienced employees into reusable rules and distinguish between fixed conditions and decisions that still require case-by-case judgment?
- Task handoffs: Can the candidate break the work into connected tasks, clearly define what each step receives and produces, and specify when the next process owner should take over?
- Approval design: Can the candidate identify situations involving high-value transactions, conflicting data, or ambiguous conditions and pause the process for review by a designated person rather than allowing the AI Agent to decide on its own?
These capabilities can also be demonstrated using EgentWrX. Teams can codify the established practices of experienced employees as AI Skills and roll them out after review. They can also connect multiple tasks into a workflow and require human approval before a handoff, allowing the AI Agent to complete the initial work and then pause for confirmation. When evaluating candidates, companies can ask them to build a small segment of an actual process and test it using a normal case, a case with missing data, and an unauthorized-action case. Managers should focus not on how polished the demo looks, but on whether the rules are clearly defined and whether the process stops when an exception occurs.
Acceptance Testing Must Cover Every Step, Not Just the Final Answer
Many demos feature only cases with complete data, producing final outputs that appear reasonable. Once a system enters production, however, managers must be able to answer another set of questions: Which version of a document did the AI Agent read? What data did it use? At what step did it change its original judgment? Who approved the continuation of the process? If the team can open only the final report but cannot reconstruct the intermediate execution process, IT will struggle to identify where an error occurred, and managers will be unable to determine which stage failed.
For this reason, project acceptance criteria must include traceability. A company can designate a test case and require the candidate team to explain the data sources, execution steps, rule versions, and approving personnel on the spot. The team should also export the relevant records so that each item can be checked for completeness. EgentWrX’s audit log can record the actions performed by an AI Agent and use a hash chain to verify the integrity of those records. The system also logs when a manager downloads audit records. These mechanisms cannot guarantee that every result will be correct, but if a dispute arises later, the company will at least be able to determine what happened at the time.
If a company cannot yet clearly define the team’s responsibilities, there is no need to rush into establishing an FDE team. It can begin with a single cross-functional process and ask candidates to complete a data inventory, define rules, design task handoffs and human approvals, and export execution records within a limited scope. The process owners, IT, and management can then conduct acceptance testing together. The AI deployment talent companies need today must be able to turn employees’ experience into workflows that can be reviewed and approved—and whose execution history can be traced afterward.
FAQ
Does an FDE have to be a senior software engineer?
It is advisable to begin with candidates who have systems integration and hands-on implementation experience, but job title is not the only criterion. The assessment should determine whether candidates can interview process owners, break down processes, handle exceptions, and clearly explain data and access requirements to IT. Otherwise, even a highly capable engineer may find the project stalled by cross-functional coordination challenges.
How should internal staff and external engineers divide responsibilities during the 12-week implementation period?
Internal staff should be responsible for explaining business rules, data ownership, responsibilities, and approval conditions, while external engineers should assist with system design, integration, and testing. Both parties should clearly define the expected deliverables for each week, who has decision-making authority when problems arise, and which changes require management approval. This prevents the organization from being left without anyone capable of maintaining the system after the project ends.
What type of process should be selected for the assessment exercise?
Prioritize processes with clearly defined inputs and outputs that span at least two departments and still require human judgment. Vendor document reviews, report consolidation, and customer service case classification are all suitable options. Processes should not yet be used as assessment exercises if roles and responsibilities remain unclear, data sources are unstable, or a single error could immediately cause significant losses.
How can a company verify that an AI Agent is truly ready for production?
Test it systematically with normal cases, missing-data cases, conflicting-data cases, and unauthorized-action cases. Require the team to reconstruct the data sources, execution steps, and approval records. In addition to reviewing the final answer, confirm that the process pauses when an exception occurs and that designated personnel can access a complete record of all actions.
References
- Technical Skills Are Not Enough: An Anthropic Executive Reveals Five Traits of Strong FDEs, Including Curiosity — TechOrange summarizes Anthropic’s FDE training program, talent requirements, and observations about the skills gap.
