WORKFLOX Services
AI Agents That Do Real Work
Not Just Answer Questions, execute Tasks
Traditional chatbots answer questions. AI agents execute tasks. We build agents that can look up your CRM, create tickets, send emails, update databases, and route decisions, all without human intervention.
- What we build
- Single-workflow and multi-step AI agents with human review on exceptions
- Typical cost
- $4,000 to $41,000, fixed price against a written scope
- Timeline
- 4 to 14 weeks, depending on integrations
- Stack
- Claude, OpenAI, Gemini and LangGraph
- Engagement
- Fixed price with 60 days of support after launch
The Challenge
Chatbots Are Not Agents
- βRule-based chatbots break on any input outside the script
- βNo real-time access to your internal systems or live data
- βCan't take actions, only answer pre-defined questions
- βNo memory of previous conversations or user context
- βRequire constant manual updates to stay current
Our Approach
What AI Agents Can Do
- βReason about the user's goal, not just their literal words
- βAccess your CRM, database, and APIs in real time
- βExecute multi-step tasks: search β decide β act β confirm
- βMaintain conversation context across sessions
- βEscalate to a human with full context when appropriate
Use Cases
What We Build For You
Customer Support Agent
Resolves Tier-1 support tickets by accessing your knowledge base, looking up order status, and triggering refunds, without a human.
Sales Copilot
Qualifies inbound leads, books discovery calls, and populates your CRM, available 24/7 on your website and WhatsApp.
Internal Knowledge Agent
Lets your team ask questions and get answers from your internal wiki, Notion, Confluence, or PDF documentation.
Contract and Legal Assistant
Reads, summarises, and extracts key clauses from contracts. Flags non-standard terms and routes for human review.
Finance Agent
Answers finance team questions about invoices, budgets, and spend, pulling live data from your accounting system.
Multi-Agent Orchestration
Multiple specialised agents working in sequence: an intake agent qualifies the request, a research agent gathers data, an action agent executes, coordinated automatically.
Our Process
Step-by-Step Development Process
01
Agent Strategy & System Audit
We map out your business workflows, define human-in-the-loop triggers, and plan agent tools.
02
LangGraph Architecture Design
We construct agent state graphs, loop flows, decision paths, and memory retention mechanisms.
03
Tool & API Implementation
We build custom interfaces allowing the agent to read/write to your CRM, databases, and third-party APIs.
04
Evaluation & Guardrail Setup
We implement safety guardrails, check LLM output schemas, and evaluate agent performance.
05
Deployment & Human-In-The-Loop
We deploy the agent to your live environment, setting up smooth routing to human agents when needed.
Technology
Our Stack
Agents here are built on a model-agnostic orchestration layer (LangGraph or LangChain), a retrieval index in Pinecone or Weaviate, tool calls behind narrow credentials, and n8n for the workflow glue. The model provider is a setting rather than an architecture, so it can change when a better option ships.
FAQ
Frequently Asked Questions
What is the difference between an AI agent and a chatbot?
A chatbot follows pre-defined rules and can only answer questions it was programmed for. An AI agent uses a large language model to reason about a goal, decide which tools to use, and execute multi-step tasks autonomously: like looking up your CRM, creating a ticket, and sending a confirmation email in one conversation.
How do you connect AI agents to our internal systems?
We define tools (API calls, database queries, webhook triggers) that the agent can call during a conversation. The agent decides which tools to use based on the user's request. We connect to any system with a REST API, including your CRM, ERP, ticketing system, and databases.
Can AI agents handle Arabic and English conversations?
Yes. Models like GPT-6 and Claude have strong Arabic language capabilities. We build agents that detect language and respond appropriately, switching between Arabic and English within the same conversation.
Are AI agents reliable enough for enterprise use?
Enterprise-grade agent reliability requires: strict output validation, fallback to humans on low-confidence responses, full conversation logging, and regular evaluation against test cases. We implement all of these as standard.
How much does it cost to build an AI agent?
A focused single-purpose agent (e.g., customer support or lead qualification) typically costs $4,000β$10,000. Multi-agent orchestration systems for enterprise use typically range from $13,500β$41,000 depending on the number of integrations and agents.
What do your AI agent development services include?
The full system: scope, retrieval over your content, permission-scoped tools, an evaluation set from your real cases, a human-approval boundary, per-run tracing, compliant hosting and the first post-launch iteration. At a fixed price against a written scope, with inference costs modelled before you commit.
Custom AI Agent Development Company in USA, UAE & Saudi Arabia
WORKFLOX designs and develops autonomous AI agents that act on behalf of your business. Unlike traditional chatbots that simply answer predefined questions, our AI agents reason, plan, and execute multi-step operations. We integrate agents with CRM platforms, ERP systems, and communication channels (such as WhatsApp, Slack, and email) to automate high-friction operational workflows across Dubai, Riyadh, Abu Dhabi, and global tech centers.
Current LangGraph & LangChain Agent Architectures
We construct reliable multi-agent orchestration systems using advanced frameworks like LangGraph and LangChain. This allows us to create stateful agents that maintain memory across sessions, recover from errors, self-correct, and coordinate tasks. For instance, a customer support agent can qualify a request, search the database, trigger a refund, and update the CRM all within one conversation.
Human-in-the-Loop (HITL) Guardrails and Reliability
Reliability is the biggest challenge in autonomous systems. Our AI agents are built with strict safety boundaries and human-in-the-loop verification triggers. If the agent's confidence score falls below a set threshold, it escalates the interaction to a human support agent along with the full chat history and context, maintaining a smooth experience.
Types of AI Agents We Build
Not every problem needs an autonomous agent, and the word covers very different systems. Single-task agents handle one bounded job well: qualifying an inbound lead, triaging a support ticket, reconciling an invoice against a purchase order. Multi-step workflow agents chain several tools together and carry state between them, such as an agent that reads an email, extracts the order details, checks stock in your ERP, and drafts a reply for approval. Multi-agent systems assign specialised roles, a researcher, a writer, a reviewer, and coordinate them through a supervisor. Finally, copilot-style agents sit inside an existing interface and assist a human rather than replacing the step. We scope which of these you actually need in the first week, because the cost difference between them is substantial and most teams are quoted the most expensive option by default. If you are weighing this against a simpler conversational build, our AI agent vs chatbot comparison lays out where the line falls.
Choosing the Right Model: GPT-6, Claude or Open-Weight
Model choice is an engineering decision with direct cost consequences, not a branding preference. Frontier models from OpenAI and Anthropic handle complex reasoning, long context and reliable tool calling, and they are the right default for agents that must not get things wrong. Smaller and open-weight models run far cheaper per call and can be self-hosted for data residency, which matters for Saudi PDPL and UAE workloads that cannot leave the region. In practice most production agents we ship are hybrid: a cheap model classifies and routes, and a frontier model handles only the steps that genuinely need reasoning. That routing alone typically cuts inference spend by half or more without a measurable quality drop. We benchmark candidate models against your real data before committing, and our breakdown of OpenAI vs Claude covers how the leading options differ in practice.
RAG and Tool Use: Giving Agents Access to Your Data
An agent is only as useful as what it can see and do. Retrieval-augmented generation grounds responses in your own documents, policies and records instead of the model's training data, which is what stops an agent inventing an answer about your refund policy. We build retrieval pipelines with proper chunking, embeddings and re-ranking, and we evaluate retrieval quality separately from generation quality. A step most implementations skip, and the most common reason a demo that worked falls apart in production. Tool use is the other half: giving the agent typed, permissioned functions to query your database, call your CRM, or trigger a workflow, each with its own access boundary. We connect agents to the systems you already run rather than asking you to migrate, and our LLM integration and data engineering work often runs alongside an agent build for exactly this reason.
Evaluation, Observability and Why Agent Projects Fail
Most failed agent projects did not fail at the prototype stage. They failed because nobody could tell when the agent started getting worse. We build an evaluation set from your real cases before writing the agent, so there is an objective score to move. Every production agent ships with tracing on each step, logged tool calls and inputs, cost and latency per run, and alerting when the failure rate or spend crosses a threshold. That means when a model provider silently updates a version or your data shifts, you find out from a dashboard rather than from a customer. We also define the escalation path explicitly: what confidence threshold hands the interaction to a human, what context travels with it, and who owns the queue. Teams running agents at scale should also read our guide to building an agentic AI governance framework.
What an AI Agent Project Costs and How Long It Takes
A single-purpose AI agent, one workflow, one or two integrations, human-in-the-loop on exceptions, typically ships in four to six weeks. A multi-step agent with retrieval over your own content, several tool integrations and an admin interface generally runs eight to fourteen weeks. Enterprise deployments with on-premise or region-locked hosting, SSO, audit logging and compliance review extend beyond that. We quote a fixed price against a written scope rather than an hourly rate, because open-ended clocks on AI projects are how budgets get away from people. Ongoing cost splits into inference spend, which we model against your expected volume before you commit, and maintenance. You can see indicative figures on our packages page and a fuller breakdown in our guide to AI app development cost. If you are evaluating partners rather than approaches, our note on how to vet a custom AI agent development partner sets out the questions worth asking.
Custom AI Agent Development Services: What You Are Actually Buying
AI agent development services vary enormously in what they include, and the price difference between vendors is mostly explained by that. Ours cover the whole system, because an agent is only as useful as its surroundings: a written scope with the agent's job, tools and boundaries defined; a retrieval layer over your own content where the agent needs to know things; MCP-exposed, permission-scoped tools for anything it needs to do; an evaluation set built from your real cases and scored before launch; a human-approval boundary at a defined confidence threshold; per-run tracing of every tool call, cost and latency; deployment to hosting that satisfies your data rules; and the first post-launch iteration. Custom AI agent development services quoted well below the bands on our agentic AI companies guide usually describe a prompt on a general model, which is a demo, not an agent. Our tooling standards and knowledge base methodology notes describe the two layers most often left out. Regional detail for Saudi buyers is on our custom AI agent development in Riyadh and Jeddah pages.
AI Agent Development in Saudi Arabia, Riyadh and Jeddah
Saudi buyers ask for Arabic-first agents, in-Kingdom hosting and PDPL-aligned design. We cover the national picture, including cost in riyals, on our page on custom AI agent development in Saudi Arabia, with city detail for custom AI agent development in Riyadh (enterprise and government-adjacent work) and custom AI agent development in Jeddah (trade, ports and logistics).
Markets We Serve
Localized Expertise Across High-Value Regions
Industries We Serve
Industry-Specific Expertise
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Send us the workflow you want an agent to run, the systems it must touch and who needs to approve its actions. We will reply with an approach, an evaluation plan and a fixed price.
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