AI Agent Development
AI Agent Development Services to Enhance Your Applications with Powerful AI Capabilities
Our AI agent development services build systems that don't just answer questions — they take action: pulling data, calling APIs, updating records, and completing multi-step workflows your team currently does by hand.
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The Problem With Chatbots That Only Talk
A chatbot that answers questions is useful. A chatbot that still requires a human to go do the actual work afterward is a productivity tool with a ceiling. Most "AI features" stop at the conversation — they tell you what to do next instead of doing it, because real workflow automation with AI agents means reliable tool-calling, error handling, and multi-step planning that most teams underestimate the difficulty of building correctly.
What Is an AI Agent, and Why Not Just a Chatbot?
Chatbot / Assistant
Best for: Answering questions, summarizing, drafting content
Takes action: No — output is text for a human to act on
Multi-step tasks: Limited — single-turn responses, no planning
Failure mode: Wrong or incomplete answer, caught by the user reading it
Typical build complexity: Lower
AI Agent
Best for: Multi-step workflows — lookup, decide, act, verify, repeat
Takes action: Yes — calls tools, APIs, and external systems directly
Multi-step tasks: Core capability — plans and executes a sequence of steps
Failure mode: Can take a wrong action, so error handling and guardrails are critical
Typical build complexity: Higher — requires careful tool design and failure recovery
If your use case is "summarize this" or "answer this question," a chatbot is enough and cheaper to build. If it's "go check three systems, reconcile the data, and update the record," you need an agent. Our AI Strategy & Architecture Audit will tell you definitively which one fits before you commit budget to either.
What We Build
Single-agent workflow automation for well-defined repetitive tasks (data entry, report generation, status checks)
Multi-agent systems using LangGraph or CrewAI, where specialized agents hand off subtasks to each other
Tool-use integration — agents that call your internal APIs, databases, and third-party services reliably
Human-in-the-loop checkpoints for high-stakes actions that need approval before execution
Error handling and retry logic so a failed API call or unexpected input doesn't silently break the workflow
Observability — logging every agent decision and tool call so you can see exactly why an agent did what it did
Ongoing ops retainer for ongoing tuning as your tools, APIs, or business logic change
Who This Is For
SaaS companies wanting to automate customer support resolution beyond simple FAQ answering
Logistics and operations teams needing multi-step reconciliation across multiple internal systems
Finance and compliance teams automating document review and approval workflows with human sign-off built in
Startups wanting an internal ops agent that handles repetitive multi-step tasks without hiring for the role
Trusted Across 50+ Countries
Codersarts maintains a 4.9/5 client satisfaction rating across hundreds of engagements. Clients consistently cite reliability under deadline pressure — Li (China) pointed to the team's patience and thoroughness even on a complex, multi-part project, while Jing (China) highlighted how knowledgeable and dependable the team was throughout.
Results
A logistics SaaS platform automated its invoice reconciliation workflow with a multi-agent system, cutting manual processing time by roughly 65%.
An e-commerce company deployed a customer support agent that resolves a majority of tier-1 tickets end-to-end without human escalation, including order lookups and refund processing.
A financial services firm built a compliance-checking agent with human-in-the-loop approval, cutting document review turnaround from days to hours.
(Client names withheld under NDA; case studies available on request.)
Pricing
Starter
Scope: Single-agent workflow automation, one core tool integration
Price: $10,000–$20,000 + $500/mo ops retainer
Production
Scope: Multi-agent system, multiple tool/API integrations, error handling
Price: $20,000–$35,000 + $1,000/mo ops retainer
Enterprise
Scope: Complex multi-agent orchestration, human-in-the-loop approval flows, full observability
Price: $35,000–$50,000+ + $2,000/mo ops retainer
For context: custom AI agent builds in the US market range from $8,000 to $400,000+ depending on integration complexity, with monthly operating costs from $65 to $20,500+. Our pricing reflects high-quality offshore delivery at a fraction of that for comparable scope.
How We Work
Workflow mapping (Week 1) — document the exact steps, tools, and decision points the agent needs to handle
Build (Weeks 2–6) — agent architecture, tool integrations, error handling
Pilot (Week 7) — run against real workflows in a sandboxed environment, fix edge cases
Launch & retainer — production deployment, ongoing tuning as tools and logic evolve
Why Codersarts
As a LangGraph development company, we design for the failure modes most teams discover only after launch — a tool call that times out, an unexpected input format, an action that needs a human checkpoint before it executes. You get a fixed-scope engineering engagement with observability built in from day one, not a fragile demo that breaks the first time a real-world edge case hits it.
Related Services
RAG Engineering & Deployment — when your agent needs to ground its decisions in your internal documents
LLM Evaluation & Benchmark Engineering — agentic systems need evaluation approaches beyond standard chatbot scoring
MLOps / LLMOps Infrastructure — for production monitoring once your agent is live
AI Strategy & Architecture Audit — if you're unsure whether you need an agent or a simpler chatbot
Get Started
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FAQ
How long does a typical AI agent build take? Starter tier: 2–3 weeks. Production tier: 5–6 weeks. Enterprise tier: 8–10 weeks depending on the number of systems being integrated.
What happens if the agent calls a tool incorrectly or an API fails? Error handling and retry logic are built into every tier. Production and Enterprise tiers add full observability so you can see exactly what the agent attempted and why.
Can the agent require human approval before taking high-stakes actions? Yes — human-in-the-loop checkpoints are a core feature of the Enterprise tier, and can be added to Production tier on request.
Do we need CrewAI or LangGraph specifically? We're framework-agnostic and choose based on your use case — LangGraph for complex stateful workflows, CrewAI for role-based multi-agent collaboration. We'll recommend the right fit during the architecture audit.
What happens after launch? The ops retainer covers ongoing tuning as your internal APIs, tools, or business logic change — agents that aren't maintained tend to degrade as the systems around them evolve.