AI Agent Development Company
We design, build and deploy production-ready AI agents that automate real business workflows, not demos. From custom AI agents to multi-agent systems, Source Code Lab has shipped 30+ agents into production across 13 industries, cutting costs and replacing manual work with reliable automation. We own the full lifecycle: scope, build, harden and operate.
AI Agent Development Services We Offer
Our AI agent development services cover the full lifecycle of custom AI agents and multi-agent systems: strategy, build, integration and operations. Every agent is scoped to a defined workflow, with permissions, tools and approvals designed in from day one.
Custom AI Agent Development
Purpose-built AI agents scoped to one workflow, with defined permissions, tools and approvals. Built to run in production, not to demo.
Multi-Agent System Development
Coordinated agent teams where specialized agents delegate, validate and hand off work for complex, multi-step operations.
AI Agent Integration
We connect agents to your existing stack through APIs, events and connectors, so they act inside your real systems of record.
AI Agent Consulting and Strategy
We identify high-value agent use cases, assess data readiness and hand you a costed roadmap before any build begins.
RAG and Knowledge Grounding
Agents that answer from your trusted data with source traceability and permission-aware retrieval, so outputs stay accurate.
AgentOps and Monitoring
Evaluation, guardrails, observability and cost control that keep agents reliable and accountable long after launch.
Core Capabilities of the AI Agents We Build
Production AI agents need more than a prompt. Every agent we build is engineered around the capabilities that make it reliable inside a real business.
Context-aware reasoning
Agents interpret tasks using your business records, policies, prior workflow state and permitted data.
Planning and task decomposition
They break a goal into controlled steps, pick the next allowed action and adapt when conditions change.
Reliable tool use
Agents call approved APIs, databases and services, with validation before any consequential action.
Memory and state
Short-term and governed long-term memory with clear rules for retention, access and updates.
Human in the loop
Agents pause for review, present source-linked evidence and route exceptions when judgment is needed.
Traceability
Every input, source, tool call and approval is logged, so you can review exactly how an outcome was produced.
AI Agents We Have Shipped, By Industry
We do not sector-lock. These are real production agents, running for real operators. Each links to the full case study.
Fraud and ops agents for iGaming
A 10-agent system for a casino and sportsbook that replaced a 15-person ops team and stops fraud in real time.
MagicianBet case study HospitalsCommission automation for hospitals
Doctor-commission processing across 45 specialities, cut from 23 days to minutes and correct every time.
KD Hospital case study Oil and GasInventory agents for field operations
An inventory agent tracking 35,000+ parts across remote sites, trained on the field's own vernacular.
Deep Industries case study JewellerySales agents for retail growth
A six-agent sales engine for a USD 50M jeweller, automating scraping, enrichment and outreach.
Suvarnakala case study PharmaHR and payroll agents for pharma
An AI-native HRMS letting one manager run HR for 1,000 employees, with payroll accurate in minutes.
Antilla Lifesciences case study More25+ clients across 13 industries
From manufacturing to logistics to financial services, see the full portfolio of production AI agents.
All case studiesOur AI Agent Development Process
Every engagement runs one shape, whatever the scope. The third phase is what separates an agent your business depends on from a demo.
Scope and spike
A paid discovery week. We pressure-test feasibility on your real data and write success criteria in numbers, then give you a fixed scope and price.
Prototype
A working end-to-end agent in front of real users in 2 to 6 weeks, not a slide deck. This is where wrong assumptions surface early.
Harden
Evaluation sets, guardrails, permissions, cost controls and observability. The work that separates a demo from production.
The phase that mattersOperate and hand over
We run the agent while your team learns it, then hand over docs and runbooks. You own the code from day one.
Our AI Agent Development Tech Stack
We are model-agnostic. We pick the right models, frameworks and infrastructure for your use case, your enterprise standards and your operating cost.
Models
Agent frameworks
Knowledge and data
Integration and ops
Why Choose Us for AI Agent Development
Production first, not demos
We measure success by agents that run in production and keep working. Our third phase, hardening, is built into every engagement, not sold as an add-on.
Engineer-led delivery
You talk to the engineers building your agent, not account managers relaying requirements. Every claim on this page maps to a real deployment.
Governed by design
Permissions, approvals, audit trails and stop controls are designed into the agent from day one, so you keep authority over consequential actions.
Proof across 13 industries
From iGaming fraud detection to hospital commission automation, we have shipped 30+ agents with measurable outcomes you can verify in our case studies.
Real Agents. Measurable Results.
How Much Does AI Agent Development Cost?
A production AI agent typically reaches first deployment in 2 to 6 weeks, with cost scoped to a fixed price before any build.
The final figure depends on how many systems the agent integrates with, how ready your data is, how much autonomy it needs and your compliance requirements. We never run open-ended retainers. After a paid discovery week you get a fixed scope and price, so you know exactly what you are committing to.
AI Agent Development FAQs
Straight answers to what enterprise buyers ask before hiring an AI agent development company.
Book a strategy callAI agent development is building software that acts, not just answers.
It is the process of designing, building and deploying agents that reason, use tools and complete multi-step tasks against your real systems, with permissions and oversight built in.
A chatbot answers; an AI agent acts.
A chatbot responds to messages. An AI agent plans, calls tools and APIs, and completes real work inside your systems, which is what makes it useful in production.
Most production agents reach first deployment in 2 to 6 weeks, at a fixed scoped price.
Cost depends on integrations, data readiness and autonomy level. We scope a fixed price after a paid discovery week, so there are no open-ended costs.
Typically 2 to 6 weeks to first deployment.
We ship a working end-to-end slice early, then harden it with evaluation, guardrails and monitoring, rather than disappearing for months.
Yes. We are model-agnostic.
We build production agents on Claude, GPT and open models, choosing the model that fits the task and your cost profile rather than defaulting to one.
Ready to build an AI agent that ships?
Tell us the workflow that is eating up your time. We will map where an AI agent fits and give you a fixed scope before any build.
Book a Strategy Call