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AI Agent Implementation: A Practical Checklist for Getting From Pilot to Production

By OMEGACORTEX LLC ·

Many AI agent projects don't fail because the model is weak. They fail because nobody decided what "done" means, the agent got more access than it needed, or nobody could explain afterward why it did what it did. In June 2025, Gartner predicted that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls (Gartner).

Those three causes can be managed. This checklist covers what we look at before an agent is allowed to do real work. It reflects how we design our own agent pipeline at OMEGACORTEX and the public guidance listed at the end. It isn't a client case study.

1. Start with one workflow, not "AI strategy"

Pick a single process that is:

  • Frequent. It happens often enough that improvement is visible within weeks.
  • Bounded. Inputs and outputs are clear: a question in and a sourced answer out, or a PDF in and a validated record out.
  • Reviewable. A person can tell whether the output is right.
  • Low-regret on failure. If the agent gets it wrong, a reviewer catches it before it reaches a customer, a citizen or a ledger.

Good first candidates are first-line answers to internal IT questions, extracting fields from incoming documents, and drafting replies for a person to approve. Poor first candidates are anything that moves money, changes access rights or makes eligibility decisions without review.

2. Ask whether you need an agent at all

"Agent" covers a wide range of systems. Anthropic draws a useful line between workflows, where models and tools follow predefined code paths, and agents, where the model decides its own steps and tool use. Its advice is to find the simplest solution that works and add complexity only when it clearly improves results (Anthropic, "Building effective agents").

In practice, many "agent" projects are better built as workflows. Document extraction with fixed validation rules is usually a workflow. Open-ended research or multi-step troubleshooting may justify an agent. Choosing the simpler design up front lowers cost, makes testing easier and makes the system easier to explain to an auditor.

3. Write down the baseline before you build

You can't show business value without a "before" number. Before any build work, record how the process performs today:

  • Volume per week
  • Time per item, or turnaround time
  • Error or rework rate, and how it's measured
  • Who handles exceptions today

Agree on one or two success criteria, for example "turnaround under X hours with no increase in rework," and on how they'll be measured. If you can't measure the baseline, measuring it is the first deliverable.

4. Give the agent the least access that works

The OWASP Top 10 for LLM Applications (2025) lists Excessive Agency as a top risk: an agent with more functionality, permissions or autonomy than it needs can do damage when it misreads an instruction or is manipulated by malicious input, such as prompt injection hidden in a document (OWASP).

Practical rules:

  • Expose only the tools the workflow needs. A support agent that answers questions doesn't need write access to the ticketing system on day one.
  • Prefer narrow tools to general ones. Use "look up order status," not "run any SQL query."
  • Act with the user's permissions, not a super-user account.
  • Enforce authorization in the downstream system as well as in the prompt. The prompt is not a security boundary.

5. Put a human approval step where it matters

Decide in advance which actions the agent can take alone and which need a person to approve. A simple three-tier split works well:

Action typeExampleRule
Read and draftSearch documents, draft a replyAgent acts alone; output is labeled as a draft
Reversible changeCreate a ticket, fill a form fieldAgent acts and the change is logged and reviewable
Consequential or irreversibleSend to a customer, approve a payment, release codeA named person approves first

We use the same rule internally: in our software pipeline, specialized agents (analyst, architect, test writer, developer, QA and security) each own a step and check each other's work, and a person approves every release.

Low confidence should route the case to a human automatically. "I'm not sure, here is what I found" is a good agent output. A confident guess isn't.

6. Keep the source for every output

If a reviewer can't see where an answer came from, they can only trust it or reject it. They can't check it. For each output, record:

  • The input it received
  • The documents or records it used, linked
  • The tools it called and what came back
  • Who approved it, and when

This audit trail is what turns "the AI said so" into something a manager, an auditor or a contracting officer can verify. It also makes debugging possible when the agent gets something wrong.

7. Test against real cases before go-live

Build a test set from real, representative items, including the awkward ones: bad scans, ambiguous questions, missing fields, contradictory documents. Run the agent against it and compare with the baseline. Re-run the same test set every time you change the prompt, the model or a tool. Model updates can change behavior without warning.

Include a few adversarial cases too, such as a document containing instructions like "ignore your previous rules." The goal is to confirm the agent's permissions and approval steps contain the damage, not to prove it can never be fooled.

8. Plan for monitoring and handover

Production is where agents meet inputs nobody anticipated. Before launch, agree on:

  • What gets monitored: exception rate, reviewer overrides, cost per item, latency
  • Who owns it: a named person on your side, not "the vendor"
  • The off switch: how to pause the agent and fall back to the manual process
  • Documentation: runbooks, the permission list, the test set and how to re-run it

The NIST AI Risk Management Framework organizes this work into four functions: Govern, Map, Measure and Manage. It's a useful structure for deciding who is accountable for each piece (NIST AI RMF).

9. Decide what "pilot success" means before the pilot starts

A pilot that ends with "it looks promising" usually ends there. Agree upfront on the decision you'll make at the end:

  • Scale: success criteria met, risks acceptable, owner in place
  • Adjust: promising, but one specific issue to fix, with a date
  • Stop: criteria not met; keep the documentation and the baseline, and move on

Stopping a pilot that doesn't meet its criteria is a good outcome. It's cheap, and it's exactly the discipline Gartner's prediction says many projects lack.

The checklist in one place

  • One frequent, bounded, reviewable workflow selected
  • Workflow vs. agent decision made, simplest design chosen
  • Baseline measured; one or two success criteria agreed
  • Tools and permissions limited to what the workflow needs
  • Human approval defined for consequential or irreversible actions
  • Source, tool calls and approvals logged for every output
  • Test set built from real cases, including edge and adversarial cases
  • Monitoring, owner, off switch and documentation agreed
  • Scale / adjust / stop decision criteria written down

How we can help

OMEGACORTEX builds teams of AI agents where agents do the work and people set the goals and approve what matters. Our 30-day pilot applies this checklist to one process: we scope it with you, build it, measure it against an agreed baseline and hand over the results, at a fixed price quoted to your scope. If you have a workflow in mind, Book a call.

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