LLM Engineering / AI Agents
AI Agent Development
AI agents can research, decide, and act across your tools — or loop, hallucinate, and take actions nobody approved. We build agents with well-defined tools, permissions, memory, human approval where it matters, and evaluation, so they finish real work reliably.
This Is For You If...
You want an agent to handle a real workflow — support, research, operations, or data entry
Your agent prototype works in demos but fails or loops on real inputs
You need agents to use your internal tools and APIs safely
You're choosing between frameworks like LangGraph, CrewAI, or provider SDKs
Which Area Matches Your Need?
Ask Yourself | Points Toward |
Are you defining what the agent should do? | Agent Design |
Does the agent need your tools and data? | Tool & API Integration |
Can the agent take risky actions? | Guardrails & Human Approval |
Do you need to know how often it succeeds? | Agent Evaluation |
AI Agent Services We Offer
Agent Design
Breaking a workflow into steps, deciding where an agent helps and where fixed logic is better, and choosing single or multi-agent designs.
Tool & API Integration
Giving agents scoped access to your APIs, databases, and SaaS tools, including MCP servers where useful.
Guardrails & Human Approval
Permission limits, input and output checks, spending limits, and approval steps for sensitive actions.
Agent Evaluation
Test scenarios that measure task success, cost, and failure modes before and after each change.
What We Need From You
What to Share | Why It Helps |
The workflow and its steps today | Defines what the agent must do |
Tools and data it should use | Shapes integrations and permissions |
Examples of real tasks | Used to build evaluations |
Risk limits | Determines guardrails and approvals |
Common Mistakes to Avoid
Instinct | Why It Doesn't Help | Do This Instead |
Letting the agent do everything | Hard to control and debug | Use agents only for steps that need judgment |
Giving broad tool access | One mistake can do real damage | Scope every tool and permission |
Judging by a few demos | Failures appear on real variety | Evaluate on many real tasks |
No cost limits | Loops can run up large bills | Cap steps, tokens, and spend |
How It Works
1. Map the workflow
Steps, tools, and decisions
2. Design the agent
Architecture, tools, and guardrails
3. Build
Integrations and agent logic
4. Evaluate
Success rate, cost, and failures measured
5. Deploy and monitor
Logging, tracing, and alerts
Why Codersarts
We build agents for reliability, not demos — scoped tools, approvals where actions matter, and evaluations that show how often the agent actually succeeds.
Turnaround Time
A focused agent for one workflow is typically built within two to three weeks. Multi-agent systems are scoped individually.
FAQ
Which models do you use?
OpenAI, Anthropic Claude, Google Gemini, and open-source models, chosen per task and cost.
Do agents need a knowledge base?
Often — see RAG Systems.
Can agents act inside our existing software?
Yes, through APIs and tool integrations with scoped permissions.
Is our project kept confidential?
Yes. Your data, code, and results stay confidential, and an NDA is available on request.
Related Services
Want an Agent That Finishes Real Work?
Build it with the tools, limits, and testing it needs.