AI Agent Development in Riyadh, done the way it should be: scoped small, measured on real usage, and handed over with docs and runbooks your engineers can read.
For Riyadh companies, we treat ai agent development as engineering — versioned, tested, monitored — not as a science project you renew every year. Most briefs we see out of Riyadh come from government, banking, and Vision 2030 programmes — the vertical shifts, but the shape of the problem does not. That means agents that take real actions inside your systems, with logs, permissions, and a rollback path, with clear ownership of what runs in production and who fixes it when something breaks. Every project ships with docs, evals, and a runbook the next engineer can pick up cold, without a knowledge-transfer week. We work in your timezone, we speak the vendor landscape in Saudi Arabia, and we know which cloud regions actually keep data on-shore. What sets our ai agent development delivery apart is that the engineer who scopes the build is the same engineer who ships it and shows up at the go-live call. If the project has already stalled once, the shape of the first release was usually wrong — that's fixable in a week, not a quarter.
The Saudi capital and Vision 2030 core is competitive, and Riyadh operators don't get credit for AI theatre. What ships and reduces cost — or lifts revenue — is what earns the next budget round, and that's what we optimise for.
Agents that draft the email, book the meeting, update the record, or open the ticket — with a clear audit log of what changed, when, and why.
Every tool call is scoped to what the agent is allowed to do, and destructive actions require confirmation until they've been shown to be safe. No blast radius.
When an agent misfires — and they will — you can see exactly what it did and undo it. That's a product requirement for us, not an afterthought.
Agents are evaluated on whether the task finished correctly, not on whether the tokens read nicely. We track completion rate as the headline number.
Research a prospect, draft a personalised outreach, log it in the CRM, book the meeting. The rep reviews and clicks send.
Triage inbound tickets, look up related history, take a first pass at a resolution or escalate, and update the record.
Agents that watch for schema drift, adjust downstream mappings, and open a PR for human review before anything ships.
For Riyadh clients delivering Vision 2030 mandates, we build AI agents that clears NCA and SDAIA guidance, sits in a Saudi-region cloud, and integrates with the Tier-1 banking and ministry stack that most programmes already run on.
Less than the sales pitch. We ship agents that operate inside a well-defined scope with permissions, logs, and human approval for anything reversible or expensive. Fully autonomous agents make sense for narrow, low-stakes workflows. For anything that touches customers, money, or production data, the agent is a copilot, not a replacement.
LangGraph and OpenAI's tool-calling APIs cover most of what we build. For long-running, multi-step workflows with retries and durable state we bring in Temporal. The framework isn't the interesting part — the interesting part is defining tools carefully, writing eval tasks that reflect real work, and instrumenting so you can see what the agent tried and why.
Least-privilege tool design, dry-run mode by default for destructive actions, mandatory approval for anything above a threshold, and a full audit log of every tool call. We also run agents against a red-team eval set that specifically tries to trick them, and any regression blocks the release.
A single-purpose agent (one workflow, one system to touch) is usually four to six weeks. Multi-tool agents that operate across several systems take eight to twelve. Most of the work isn't the model — it's defining tools cleanly, building eval tasks, and integrating with the source systems safely.
Yes, as long as the system has an API or a stable UI. We prefer APIs, obviously, but we can also drive UIs when that's the only path. For enterprise systems (Salesforce, Dynamics, SAP, ServiceNow, etc.) we've integrated most of the common ones and can move quickly.
Task-completion rate on a labelled eval set is the headline number. Alongside that we track cost per task, latency, human-intervention rate, and error class breakdown so you can see where the agent needs work. Everything lands in a dashboard your team owns after handover.
Yes. For Riyadh clients we default to Saudi-region cloud (AWS or GCP in KSA), work with local Saudi partners where Saudization requires it, and design for NCA and SDAIA guidance from the start of the engagement. The regulatory shape is treated as a delivery input, not something we discover at UAT.
SM Stratagem builds llm development in Riyadh, Saudi Arabia. LLM apps that survive production. Cost and latency instrumented. Prompts versioned and evaluated.
SM Stratagem builds ai integration services in Riyadh, Saudi Arabia. AI inside the systems you already run. CRM, ERP, help desk, product. Book a scoping call.
SM Stratagem builds ai consulting in Riyadh, Saudi Arabia. Strategy that ends in a build. Roadmaps you can budget. Vendor-agnostic advice. Book a scoping call.
SM Stratagem builds ai agent development in Dammam, Saudi Arabia. Agents that take real actions. Permissioned and logged. Rollback baked in.
SM Stratagem builds ai agent development in Dubai, United Arab Emirates. Agents that take real actions. Permissioned and logged. Rollback baked in.
We'll scope the first release, define the eval set, and give you a build plan you can hand to any engineering team — ours or yours.