Production AI Agents: From Demo to Real Execution

A demo optimizes for the happy path: curated inputs, a forgiving audience, and a human silently correcting course. Production removes all three. The agent meets malformed tickets, adversarial users, flaky APIs, and inputs nobody anticipated — and there is no author standing by to nudge it back.

The failure modes are predictable. An agent with write access commits a bad record and nobody notices for a week. A retry loop burns a month of token budget overnight. A prompt tweak fixes one case and silently regresses ten others. None of these are model problems; they are missing-infrastructure problems. Teams that ship successfully treat the model as one component inside a system that constrains, observes, and measures it — the approach the Dezifi platform is built around. Teams that fail keep polishing the prompt and hoping.

The first production move is giving the agent a fixed shape. In Dezifi an agent is a configured unit: one LLM, an explicit list of tools it may call, a memory profile, and a guardrail profile. Nothing is implied. If the agent can touch Salesforce, that is because someone attached the Salesforce tool — not because the model improvised an API call.

This sounds restrictive and that is the point. When capability is declared rather than emergent, you can reason about blast radius before an incident instead of after. It also makes review possible: a security team can read an agent definition the way they read a firewall rule. Keep single agents for single jobs — ticket triage, one Slack responder — and reach for multi-agent workflows only when you genuinely need branching, fan-out, or coordination between specialists.

“You must never delete records” in a system prompt is a suggestion, not a control. Models can be jailbroken, confused, or simply wrong. Production governance has to live outside the model, enforced at runtime on every action.

That is what policy-as-code gives you: declarative rules for tool allow/deny lists, data access, rate limits, cost ceilings, and wall-clock time limits, checked before each agent action. A run that hits its cost ceiling halts with a structured violation event rather than quietly overspending. Guardrails complement this at the content level — scanning prompts, tool arguments, and outputs for PII, injection attempts, and off-topic drift in real time. Policies define what the agent can do; guardrails police what it says and sees. You need both, and neither belongs in the prompt.

The demo-era release process is “it looked fine when I tried it.” The production process is a gate: run the candidate version against a golden dataset and block the publish if the success rate drops. Eval supports this as a CI-style check — simulation against synthetic scenarios during build, A/B comparison against the live version before release, and drift detection sampling real traffic after.

The discipline matters more than the tooling. Start a golden dataset on day one, even with twenty examples pulled from failing runs. Every incident becomes a test case. Within a quarter you have a regression suite that catches the prompt change that would have broken refund handling — before a customer does. Gut feel does not scale past the first agent; datasets do.

Finally: never ship an agent you cannot replay. Every production run should record its full trace — each LLM call, each tool invocation, timing, token counts, and dollar cost — so that when a customer escalates, you open the run and see exactly what happened rather than guessing.

This closes the loop. Failing runs found in monitoring become eval dataset entries. Policy violations reveal where governance is too loose or too tight. Cost per run, visible from the first test, keeps the pilot honest when finance asks what scaling costs. Demos end when the meeting ends; production agents generate evidence continuously. Build the evidence pipeline first, and the path from demo to dependable execution stops being a leap of faith.