Everyone wants to build AI agents now. The technology is accessible. The promise is compelling. The pressure is real.
But most first agents fail. Not because the AI doesn't work. Because the strategy wasn't clear.
Before you write any code, before you choose any platform, before you design any prompts - you need to answer strategic questions that determine success or failure.
What Problem Does This Agent Solve?
Start here. Not with capabilities. With problems.
"We should have an AI agent" is not a strategy. "Our invoice matching process takes 40 hours weekly and has a 3% error rate" is a problem worth solving.
The best first agents solve problems that are:
Repetitive. The same basic process happens many times. Variation exists, but the pattern is consistent. AI handles patterns well.
Measurable. You can define success. Time saved. Errors reduced. Throughput increased. If you can't measure the current state, you can't prove improvement.
Bounded. The scope is clear. The inputs are defined. The outputs are specified. Edge cases exist but don't dominate.
Low-catastrophe. Mistakes are correctable. No single error causes irreversible harm. The cost of getting it wrong is manageable while you learn.
If your proposed first agent doesn't meet these criteria, find a different problem. The goal isn't to build the most impressive agent. It's to build one that succeeds.
What Can the Agent Actually Do?
Define capabilities precisely. Not aspirationally.
An invoice matching agent might: read invoice data, compare against purchase orders, flag discrepancies, route matched invoices for approval.
It probably shouldn't: negotiate with vendors, change payment terms, approve expenditures above threshold, communicate with external parties.
Write down what the agent can do. Write down what it can't do. Make the boundary explicit. When scope creep happens (and it will), you have a reference point for the original design.
Permissions matter. What systems can the agent access? With what authority? Read-only? Read-write? Approval authority up to what amount?
Integrations matter. Where does input come from? Where does output go? What happens if integrations fail?
Dependencies matter. What does the agent need to function? If the AI API is slow, what happens? If the database is locked, what happens?
What Happens When It's Wrong?
AI agents will make mistakes. Your design must account for this.
Error detection
How do you know when the agent made a mistake? Some errors are obvious (system crash). Others are subtle (incorrect classification that looks plausible).
Error correction
Once detected, how is the mistake fixed? By whom? With what process?
Error learning
Does the mistake improve the system? Can you feed corrections back to improve future performance?
Error communication
Who needs to know when mistakes happen? How quickly? Through what channels?
The firms that succeed with AI agents don't pretend errors won't happen. They design for errors from the start.
Where Does Human Judgment Apply?
AI agents work alongside humans, not instead of them. Design the boundary.
Automatic zone: The agent handles these cases without human review. High confidence, low stakes, clear patterns.
Review zone: The agent makes a recommendation but humans approve. Medium confidence, medium stakes, some ambiguity.
Escalation zone: The agent flags the case for human handling. Low confidence, high stakes, significant ambiguity.
Define these zones explicitly. What confidence threshold triggers escalation? What case characteristics require human review regardless of confidence?
Review fatigue is real. If humans must review everything, the agent provides little value. If humans rubber-stamp agent recommendations, you've lost the oversight benefit.
Find the balance. Adjust based on experience. Start with more human oversight and reduce it as trust builds.
How Do You Know It's Working?
Before launch, define success metrics. Not after. Before.
Throughput: How many cases can the agent handle? Per hour? Per day?
Accuracy: What percentage of decisions are correct? How is "correct" defined?
Speed: How long from input to output? Is latency acceptable for the use case?
Coverage: What percentage of cases can the agent handle versus requiring human fallback?
Cost: What does it cost per case? How does this compare to the previous approach?
Establish baselines before deployment. Measure the current process. Then measure the AI-assisted process. The comparison tells you if you succeeded.
What Infrastructure Do You Need?
AI agents require supporting systems.
Monitoring
How do you know the agent is running? Is it healthy? Is performance degrading?
Logging
What does the agent do? Every decision, every action, every data access should be recorded.
Alerting
When something goes wrong, who finds out? How quickly?
Rollback
If the agent fails, can you revert to the previous process? How quickly?
Don't build the agent and then figure out operations. Design for operations from the start.
Who Owns This?
AI agents don't run themselves. Someone must:
Monitor performance daily or weekly. Is the agent still meeting targets?
Handle escalations when the agent can't. Who answers the hard questions?
Update and improve as requirements change. Who modifies prompts, adjusts thresholds, fixes issues?
Communicate status to stakeholders. Who reports on results?
Manage vendors if external AI services are used. Who handles the relationship?
Assign ownership before you build. Unowned agents become abandoned agents.
Starting Small
Your first agent should be small. Deliberately, strategically small.
Small scope means faster iteration. You learn what works in weeks, not months.
Small scope means manageable risk. When things go wrong (they will), the blast radius is limited.
Small scope means achievable success. Demonstrating value with a narrow agent funds and justifies expansion.
The temptation is to be ambitious. Resist it. Ambitious first agents typically fail. Modest first agents that succeed lead to ambitious second and third agents.
The Strategy Summary
Before you build:
- Identify a problem that's repetitive, measurable, bounded, and low-catastrophe
- Define what the agent can and can't do
- Design for errors: detection, correction, learning, communication
- Specify human oversight: automatic, review, and escalation zones
- Establish success metrics with baselines
- Plan operational infrastructure
- Assign clear ownership
With these answered, you can start building. Without them, you're guessing.
Strategy before code. Always.