Enterprise AI Agents: From Pilot to Production
Most enterprise AI agent pilots never reach production. A practical path: identity, tool allowlists, evaluation, human gates, and AgentsAI orchestration — from sandbox to SLA.
By Emerson Amorim · Founder and Principal Software Engineer
A successful pilot proves that a model can complete a task in a demo environment. Production is a different system: identity, change control, evaluation, cost, and an owner who gets paged. Most “agent” programs die in the gap between those two worlds.
This article is the path we use at EmerSoftware. AgentsAI is the orchestration layer — seven specialized roles, handoffs, and an audit trail — not a single chat session that remembers your SAP password.
Why pilots stall
- The demo used a human’s OAuth token; production needs a machine identity with least privilege.
- The agent had unrestricted tools (“browse the web, call any API”).
- There was no evaluation set — only a recorded happy path.
- Write actions hit production-like data without a rollback story.
- No one owned the runbook when the agent was confidently wrong.
Pilot vs production
- 01Sandboxsynthetic data, no writes
- 02Evalsgolden traces + graders
- 03Stagingmasked data, HITL writes
- 04Canaryone flow, one plant
- 05SLAon-call + audit
The production checklist
1. Identity and tenancy
Treat the agent as a workload identity. Short-lived credentials, environment-scoped secrets, and a mapping from agent role → allowed systems. If SAP, Salesforce, and Oracle are in scope, the agent does not reuse a consultant’s login. That is a finding waiting to happen.
2. Tool policy before prompts
Prompts are not a control. Allowlists are. Separate read tools from write tools. Separate staging endpoints from production. Log every invocation with correlation IDs. AgentsAI encodes this as policy on the control plane so a developer cannot “just add a tool” in a system prompt.
type AgentRole = "pm" | "architect" | "developer" | "qa" | "devsecops";
type ToolPolicy = {
role: AgentRole;
env: "sandbox" | "staging" | "prod";
allow: string[];
deny: string[];
requireHuman: string[];
};
const developerStaging: ToolPolicy = {
role: "developer",
env: "staging",
allow: ["git.commit", "ci.trigger", "jira.comment"],
deny: ["sap.bapi.write", "salesforce.composite.write"],
requireHuman: ["github.mergeToMain"],
};3. Evaluation is the promotion gate
Keep a golden set of traces: “given this incident, the agent must open a PR that does not touch billing tables.” Score format, tool-choice, and hallucination of system names. If the eval set is only marketing copy, you do not have a product.
4. Human-in-the-loop where irreversibility lives
PRD approval, architecture, production deploy, and any write to ERP/CRM of record. The rest can be automated. Autonomy without a gate is a liability, not a feature.
If your PoC already works in a lab, the next conversation is production design — not another model bake-off. That is the AI strategy call.
Ready to accelerate your enterprise software?
Talk with EmerSoft about the software factory, AgentsAI, and SAP, AWS, and Azure integrations — with accelerated delivery at enterprise standard.
LinkedIn · Emerson Amorim
Your AI agent pilot is not “almost production.” It is a different system.
The graveyard of enterprise AI is full of impressive sandboxes. What production actually requires: • Identity: the agent is a first-class principal (not a shared API key). • Tool allowlists: read vs write, environment vs production. • Evaluation: golden traces, not vibes. • Human gates on irreversible actions. • Observability: every tool call is a log you can replay. AgentsAI exists because orchestration is the product. The model is a component. I wrote the path we use with international teams — diagrams and a policy sketch: https://www.emersoftware.com.br/en/blog/enterprise-ai-agents-from-pilot-to-production If you are stuck after a successful PoC, that is the conversation: https://www.emersoftware.com.br/en/book — Emerson Amorim, EmerSoftware
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