Everyone wants AI agents. Few are ready to run them in production.

AI agents are everywhere right now. Big tech is making big bets. Enterprise leaders want to expand AI agents across all business functions. 

It makes sense: a system that can read, decide, and act — update records, process documents, support customers, trigger workflows, and help teams move faster — is highly desirable.

But the gap between a working demo and a production-ready agent is bigger than most teams realize.

Not because the model isn’t smart enough, but because running an agent in production is an engineering and operations problem, not a prompt problem.

AI agents create value when they execute actual workflows — not when they generate impressive text.
To run agents in production, businesses need reliability, evaluation, monitoring, guardrails, cost control, and integrations.
This is an engineering and operations challenge, not a model or prompt challenge.

The real business pain: agents fail when expectations are too high

A demo looks great when an agent completes a task once.

In real business operations, expectations are different:

• it has to work every day, not only once
  
• it has to work inside your systems, not in isolation
  
• it has to be safe enough to trust (especially when finances, customer data, or compliance are involved)
  
• it has to be predictable in cost and performance
  
• and it has to improve over time, not quietly degrade
  

That’s why many companies end up in the same place: they can show an agent, but they can’t run it reliably.

What changes when AI starts doing work (not just answering)

When AI moves from answering questions to taking actions, the product becomes a system that must be:

• integrated with business tools
  
• safe to operate
  
• measurable
  
• reliable over time
  
• predictable in cost
  

A useful agent is not defined by how smart it sounds.
It’s defined by whether it reduces operational load, accelerates workflows, and remains secure.

What “agents in production” actually look like

Healthcare: agents that reduce admin load (without touching clinical decisions)

In healthcare, the biggest opportunities for agents are often outside the core clinical workflows — in high-volume operational workflows that overload teams.

Ideal agent workflows:

• Prior authorization support: collect required information, check completeness, prepare submission packages, and route exceptions to a team member 
• Patient intake & triage assistance: extract structured data from forms and documents, validate missing fields, and route to the right queue
• Call center / patient support: resolve routine questions, schedule appointments, and escalate based on urgency 
• Claims operations: classify documents, extract codes/fields, flag anomalies, and prepare for review

What complicates production: privacy, traceability, structured outputs, integration with EHR/CRM systems, strict guardrails.
What the system needs for success: audit logs, confidence thresholds, approval steps, and monitoring for safety and drift.

Real Estate / PropTech: agents that keep operations moving

Real estate operations are full of repetitive coordination. Agents help when they can execute work across systems — not just respond to queries.

Ideal agent workflows:

• Tenant requests: classify issue → create ticket → route to maintenance/vendor → follow-up → close with documentation
• Lease and contract intelligence: extract clauses and obligations → compare versions → flag risks → generate structured summaries
• Due diligence support: organize document sets, highlight missing items, validate key fields, generate checklists
• Property reporting: compile metrics, detect anomalies, produce weekly summaries
  

What complicates production: multiple tools and stakeholders, unpredictable input formats (PDFs, scans), and long-tail exceptions.
What the system needs for success: document pipelines (OCR + validation), structured outputs, tool boundaries, and monitoring for quality and cost.

Digital Experience: agents that execute, not just generate

In marketing and DX, agents add value when they support workflows — and when quality and brand safety are under control.

Ideal agent workflows:

• Content operations: generate drafts → check brand tone rules → validate facts → create variants → prepare publishing packages
• GEO/AEO readiness: restructure content to be AI-readable, generate Q&A blocks, build “answer-ready” knowledge pages
• Campaign operations: summarize performance → detect anomalies → propose next actions → update dashboards → draft reporting
• Customer support automation: handle repetitive questions and route exceptions — while logging outcomes and escalations

What complicates production: brand risk, hallucinations, unpredictable style drift, and the need for repeatable quality.
What the system needs for success: evaluation (tone + factual checks), guardrails, human review gates, and a clear measurement loop.

How we approach it at FLS

At FLS, we engineer AI systems that are runnable in real operations.
That includes workflow design, integrations, evaluation frameworks, monitoring, guardrails, and long-term support.

We often call this approach Accelerated AI Engineering — a structured path from idea → working system → production readiness, without skipping the hard parts that keep systems reliable.

What breaks first — and what you need

Demo agent vs Production agent

Practical checklist: Are you ready to run agents in production?

Ask these questions:

1. What workflow will the agent execute — step by step?
   
2. What tools/APIs will it use to act?
   
3. What are the stop conditions (when it must escalate)?
   
4. How do we measure success and failure?
   
5. How do we detect drift and quality decline early?
   
6. What guardrails are required (data, actions, approvals)?
   
7. What is the cost per successful task?
   
8. Who owns monitoring and improvement after launch?