Multi-step operations workflows
Agents that take an incoming request, gather data from three or four systems, make a decision and record the outcome. Order exceptions, refund checks and account changes are common starting points.
We build agentic systems that plan, call tools, and finish multi-step tasks — with the permissions, retries, and audit trails needed for them to run against production systems.
A chatbot answers a question. An agentic system takes a goal, breaks it into steps, calls the tools it needs and keeps going until the task is done or it hits a limit you set. That difference is why agentic AI development is mostly an engineering problem and only partly an AI one. The agent will touch your CRM, your ticketing system, your database and sometimes your customers. Each of those actions needs permissions, retries, a log entry and a way to undo it. We design those pieces first, then let the model plan inside them.
Our approach is conservative on purpose. We start with a workflow your team already runs by hand, such as reconciling orders, qualifying inbound leads or preparing a weekly operations report. We map every decision in it and mark which ones the agent can make alone, which need human approval and which stay off limits. Then we build typed tools around your APIs so the agent can only do what each tool allows. A planner that can call anything is a demo. A planner that can call six well-defined tools with validation is something you can run on a Monday morning.
Softzee has built software for clients since 2020, and much of our work now is AI agents and business automation for companies in the Gulf, the UK, the US and Pakistan. We work with frameworks like LangGraph and the OpenAI and Anthropic agent tooling, but we are not attached to any of them. Sometimes a plain state machine with a model call at two decision points is more reliable than a fully autonomous loop. Our agentic AI development services aim for the least autonomy that gets the job done, then expand it as the logs show the system has earned more trust.
We define what the agent may decide and where a human stays in the loop.
Agentic AI DevelopmentPlanning loops, memory, and tool interfaces designed for reliability over novelty.
Agentic AI DevelopmentTyped, permissioned tools so agents act on real systems without surprises.
Agentic AI DevelopmentAgents wired into your CRM, ticketing, and internal APIs.
Agentic AI DevelopmentTask-level scoring, rate limits, rollbacks, and full action logging.
Agentic AI DevelopmentOngoing review of traces, failure modes, and cost per completed task.
Agents that take an incoming request, gather data from three or four systems, make a decision and record the outcome. Order exceptions, refund checks and account changes are common starting points.
A coordinator that hands subtasks to specialist agents for research, drafting and checking. We only use this pattern when a single agent with good tools cannot cope, because each extra agent adds cost and failure points.
Matching payments, invoices and orders across finance and ecommerce systems, fixing simple mismatches and queuing the rest for a person. The agent writes a short explanation for every change it makes.
Agents that enrich inbound leads, score them against your criteria, update the CRM and book meetings when the fit is clear. Outbound messages can require approval until you trust the output.
Agents that pull figures from dashboards, documents and the web, then assemble a report on a schedule. Every claim in the report links back to where it came from.
Agents that handle access requests, account resets through your existing tools and routine configuration changes, with approvals built into the flow and every action recorded.
We agree in writing what the agent can and cannot do before any code is written. Those limits are enforced in the tool layer, not just in the prompt.
Each run records the plan, every tool call, its inputs and outputs, and the final result. When something goes wrong, you can see exactly which step failed and why.
We track cost and success rate per completed task, not per token. That number tells you whether the system is worth running and where to improve it.
Retries, idempotency, rate limits, queues and rollbacks are standard backend problems. Our team has handled them in production apps for years, and agent systems need every one of them.
Agents that assemble KYC files, chase missing documents and prepare cases for compliance review, with final decisions left to people.
Order exception handling, supplier follow-ups and catalog updates run across your store platform, ERP and courier systems.
Agents that watch shipments, detect delays, rebook slots with carriers and notify customers when your rules allow it.
Onboarding agents that configure new accounts, import customer data and flag setup problems before the customer notices them.
Engagement setup, conflict checks and document assembly across practice management and file storage systems.
Prior-authorization prep and referral processing, where the agent collects and checks information but staff approve every outcome.
Tell us what you are trying to build. You will get an honest take on scope, timeline and cost, usually within one business day.
Softzee built a scalable platform that makes it easy to discover venues, book wellness services, and check in across Pakistan’s growing fitness network.
Softzee designed a two-sided booking platform that makes it easy for passengers to schedule rides and for drivers to manage trips, updates, and completions in one seamless workflow.
Our team has expertise in 100+ technologies and programming languages, including the AI coding tools rewriting how software gets built.
Still have a question? Ask us directly and a senior engineer will reply.
Most agentic AI projects we deliver fall between $20,000 and $100,000 for the first production workflow. The spread comes from the number of systems the agent must act on, whether those systems have usable APIs, how much human approval is needed and how strict the audit requirements are. A single workflow touching two well-documented systems sits near the bottom. Model and hosting costs are extra and depend on task volume, so we estimate cost per completed task during design.
A chatbot responds to messages. A traditional automation follows fixed rules you define in advance. An agentic system is given a goal and decides which steps and tools to use to reach it, adapting when something unexpected comes back. In practice the best systems mix all three: fixed automation where the path is known, and agent reasoning only at the steps that genuinely need judgment.
A first workflow usually takes 8 to 14 weeks to reach production. That covers mapping the process, building and testing the tools, running the agent in shadow mode alongside your team and a staged rollout. Shadow mode is the step people want to skip and should not, because it is where most edge cases show up. Later workflows go faster, since the tool layer and monitoring are reused.
Mostly through engineering rather than prompting. Each tool has strict input validation and only the permissions it needs. High-impact actions such as payments, deletions or customer messages can require human approval. We add spending and rate limits, loop timeouts and the ability to reverse actions where the underlying system allows it, and every run is logged so problems can be traced.
We have used LangGraph, the OpenAI Agents SDK, the Anthropic Claude API with tool use and custom orchestration written in Python or Node.js. Model choice depends on how reliably it follows tool schemas and plans across several steps, which we test on your actual workflow. For many clients a small custom orchestrator is easier to debug and host than a heavy framework. We choose whatever your team can maintain after handover.
Ask how they limit what the agent can do and how they show you what it did. A credible vendor will talk about permissions, logging, approval steps and failure handling before talking about models. They should also be able to integrate with your existing systems, since that is most of the work. Ask for a pilot plan with shadow mode and clear success criteria rather than a big launch.
You own the code, prompts, tool definitions and all run logs. We deploy into your cloud account or a dedicated environment, and data stays in the region you choose, which matters for clients in Saudi Arabia, the UAE and the UK. We do not reuse your data or workflows for other clients. After launch we can support and tune the system monthly or hand it to your team with full documentation.
Tell us about your idea and we'll map out the path forward — or grab a slot and talk it through with us directly.
Book a 15min call"We're always drawn to projects that challenge us, spark creativity, and let us do our best work. Let's build something exceptional together."