AI Agent Deployment
definition
AI agent deployment is the engineering work of moving an AI agent from a demo that works on your machine to a production system — adding authentication, error handling, observability, cost controls, guardrails, and the operational scaffolding that keeps it reliable under real use.
An agent that works in a notebook is maybe 10% of the work. The other 90% is everything that makes it safe to run unattended: what happens when a tool call fails, when the model hallucinates a result, when costs spike, when two users hit it at once, when it needs to be audited later.
This is the gap where most agent projects die. The prototype is impressive; the production system never arrives because the last mile is unglamorous and genuinely hard.
Stride takes the handoff of a working agent prototype and ships it: authentication and access control, structured tool-calling with retries and fallbacks, observability so you can see what the agent did and why, cost and rate controls, and human-in-the-loop guardrails on anything irreversible.
You get a production deployment, a written ship report, and a handoff session so your team owns it — not a dependency on us.
- ·Hardening a customer-facing support agent with guardrails, logging, and escalation paths
- ·Deploying an internal research agent with auth, audit trails, and per-user cost limits
- ·Wrapping a brittle prompt chain in retries, fallbacks, and structured error handling
- ·Adding observability so you can replay any agent run and see every tool call
User / system ──▶ Auth + rate limit
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Agent orchestrator ──▶ Tools (retry/fallback)
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Guardrails / HITL Observability + logs
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Action / response Replay + audit + cost- ·Every run is logged and replayable for debugging and audit.
- ·Irreversible actions route through a human approval step by default.
- ·Cost and rate controls are built in, not bolted on after the first bill.
We built an agent that mostly works. Can you ship it?
That's the core of what we do. We take the handoff, harden the agent — auth, retries, guardrails, observability, cost controls — and put it into production with a written report and a handoff session.
Which models and frameworks do you work with?
We're model- and framework-agnostic. We work with Anthropic and OpenAI models and common orchestration approaches, and we choose based on your constraints rather than a house preference.
How do you stop an agent from doing something destructive?
Human-in-the-loop guardrails on anything irreversible, structured tool-calling with validation, and full logging so every action is auditable. You decide where the agent can act autonomously and where it must ask.