We are an agentic AI development company building agents that do real work — they plan, call your tools, read and write to your systems, and stop for a human when the stakes are high. Bounded workflows, typed tools, evals from day one.
Six capabilities that turn an LLM demo into an agentic AI system your operations team can depend on — orchestration, safe tool use, memory, oversight, observability and integration.
LangGraph or CrewAI topologies designed for your workflow — planner, executor, critic — not a single monolithic prompt pretending to be an agent.
Typed tool schemas, allowlists, scoped credentials and dry-run modes, so an agentic AI system never calls a production API it should not reach.
Short-term scratchpads, long-term vector memory and structured state with full replay — the difference between debugging an agent and guessing at it.
Approve, edit or reject each step before an AI agent touches anything irreversible. Configurable per workflow, and tightened per risk tier.
Every agent run traced in LangSmith or Langfuse with per-step cost, latency and success metrics, plus hard spend ceilings before anything reaches production.
Agents call your APIs, databases, CRMs, ERPs and browsers via MCP, function calling or custom tool adapters — we build the adapter when none exists.
The sequence we run on every agentic AI project, from mapping one workflow to graduating steps into full autonomy on the strength of evaluation data.
We pick a single high-value workflow and map every decision, tool and handoff before writing a line of agent code. Scope discipline is what separates shipped agents from demos.
Topology chosen (single-agent, planner-executor or multi-agent), tools typed, guardrails defined, and an eval set built from real historical runs rather than invented cases.
Agents run alongside your humans, proposing actions without executing them. We tune until the human-acceptance rate clears an agreed bar — usually 85%+.
Go-live with human checkpoints on every irreversible action. Steps graduate to full autonomy only as eval data earns it, never on a launch date.
Opinionated defaults for agent orchestration, memory, tracing and evaluation. We swap pieces when your workflow genuinely calls for it — no framework dogma.
Agentic AI development is the engineering practice of building software where a language model does not just generate text — it plans a sequence of steps, chooses and calls tools, observes what came back, and decides what to do next until a task is complete. The model is the reasoning engine; the agent is everything wrapped around it that makes the reasoning safe, observable and repeatable.
The distinction matters commercially. A chatbot answers a question and stops. An AI agent takes an instruction like "reconcile these invoices against the purchase orders and flag the mismatches" and works through it — querying your ERP, comparing records, writing results back, and escalating the ambiguous cases to a person. That is the difference between deflecting a support ticket and removing a recurring operational cost.
Most of the engineering effort in agentic AI development goes nowhere near the model. It goes into typed tool definitions, retry and timeout behaviour, state that survives a crash, evaluation harnesses that prove quality has not regressed, and the human checkpoints that keep an agent from doing something expensive and irreversible at 2am.
Agents earn their cost on workflows that are repetitive, multi-step, rule-heavy but not fully rule-expressible, and currently done by a person moving between three or four systems. Invoice and document processing, customer onboarding checks, data reconciliation, first-line triage, lead enrichment and report generation are all strong fits. So is anything where the bottleneck is reading — contracts, tickets, claims, compliance documents.
Agents are the wrong answer when a deterministic script would do the job, when the workflow has no tolerance for a wrong answer and no human review step is acceptable, or when the task is genuinely open-ended. "Run my business" is not a bounded workflow. We will say so on the first call rather than take the engagement.
The honest test we apply: can you describe what "done correctly" looks like precisely enough to build an evaluation set of 50 real historical cases? If yes, an agent is buildable and measurable. If not, the first project is not an agent — it is defining the workflow.
Four layers, applied together. First, typed tool schemas and allowlists — an agent can only call the functions you have explicitly given it, with arguments that validate against a schema before execution. Second, scoped credentials, so the agent operates with the narrowest permissions the task needs rather than an admin key. Third, dry-run modes and shadow deployment, where the agent proposes every action for a human to accept or reject until the acceptance rate is consistently high. Fourth, mandatory human approval on any step that is irreversible: money moving, data deleted, a message sent to a customer.
On top of that sits observability. Every run is traced step by step with inputs, outputs, tool calls, token cost and latency, so when something does go wrong you can replay exactly what the agent saw and why it chose what it chose. Agents without traces are unmaintainable — you cannot safely change a prompt if you cannot measure the effect.
We also set hard spend ceilings per run and per day. An agent stuck in a reasoning loop is a cost incident before it is a correctness incident, and the ceiling is what turns a bad night into a caught alert.
A first production agent covering one bounded workflow typically runs $40k–$120k over 6–12 weeks, depending on how many systems it has to integrate with and how much of your data is already queryable. Integration surface is the main cost driver, not model choice — building a reliable adapter to a legacy ERP costs more than the agent logic sitting on top of it.
Running costs are separate and usually smaller than teams expect. Most agents we operate land between $0.05 and $0.50 per completed task. We report that figure per workflow from the first week, because unit economics decide whether an agent should be scaled or shut off, and that decision should be made on data rather than enthusiasm.
A discovery engagement — workflow mapping, tool inventory, eval set design and a written architecture — is a fraction of a full build and produces an artefact you own regardless of who implements it. For teams unsure whether their workflow is agent-shaped at all, that is the sensible place to start. Our broader breakdown of AI build economics is in our guide to AI development costs.
We deliver agentic AI projects remotely across the UAE, Qatar, Bahrain and Saudi Arabia, and the requirements differ from a US build in ways worth planning for. Data residency is usually the first constraint — we design so regulated data stays in-region, using UAE or nearby cloud regions and zero-retention model agreements where personal data reaches a hosted provider. Arabic-language workflows need per-language evaluation before launch rather than an assumption that multilingual model support is uniform.
For Gulf clients our timezone overlap is close to complete: IST is only 1.5 hours ahead of GST, so standups, demos and incident response all sit inside your working day. We invoice in AED or USD, and we sign local paper. See our UAE and Dubai pages for how we structure those engagements.
For Australian clients the same agent patterns apply with different governance. We design to the Australian Privacy Principles, keep data in AWS Sydney, and build a human checkpoint into any step that would otherwise make a consequential decision unsupervised — the position regulators in financial services and healthcare consistently expect.
A 30-minute call. We'll talk scope, timelines, and what a realistic first release looks like. NDA signed before we start.