ArsenalTech Pvt. Ltd.
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Multi-step thinking, powered by AI, automate workflows like a human would (but faster).

Not just a smart assistant, a chain of thought agent

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  • 80+
    In-house
    Experts
  • 5+
    Team’s Average
    Years of Experience
  • 93%
    Employee
    Retention Rate
  • 100%
    Project Completion
    Ratio
Our process

How we build smart, multi-step AI agents

Workflow breakdown & goal mapping

We start by identifying your process, what outcome you want, what systems it involves, and how decisions are currently made.

Agent architecture design

We design an agent that mimics how your team would handle the task: breaking it into steps, applying tools (APIs, search, rules), and handling errors.

Tool integration & memory setup

The agent is equipped with tools like web search, database calls, internal APIs, CRM access, and document parsing, with memory and context for multi-turn reasoning.

Testing & Human-in-the-loop (HITL)

We build feedback loops where your team can review, approve, or tweak actions, ensuring safety and learning over time.

AI chain agent development

Building intelligent multi-agent systems for complex workflows

  • Tech Stack Language

    Powerful coding foundations that drive scalable solutions.

    javascript-logo-svgrepo-com

    JavaScript

    Python

    Python

    TypeScript

    TypeScript

  • Framework Extensions

    Enhanced tools and add-ons to accelerate development.

    TensorFlow

    TensorFlow

    LangChain

    LangChain

    Haystack

    Haystack

  • Cloud Services

    Secure, flexible, and future-ready infrastructure in the cloud.

    AWS

    AWS (Amazon Web Services)

    Azure

    Microsoft Azure

    SageMaker

    AWS Sagemaker

    AzureML

    Azure ML

  • Interactive Data Tools

    Smart insights and visualization that bring data to life.

    tableau-software

    Tableau

    looker

    Looker

  • LLMs & GenAI

    LLMs & GenAI

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    OpenAI

    Anthropic

    Anthropic

    llama

    Meta LLaMA

Tech talk

Developer tips & insights

Trending Blog thumbnail_RAG
4 Min read 10 September, 2025

Introduction to RAG (Retrieval-Augmented Generation) for SaaS Applications

Trending Blog thumbnail_Multitenant SaaS
4 Min read 10 September, 2025

Building Multi-Tenant SaaS Applications with Laravel: A practical guide for scaling globally

Trending Blog thumbnail_Filament Vs Laravel Nova
2 Min read 10 September, 2025

Filament vs Laravel Nova: CTO Guide

AI chain agent development

Automating complex workflows with intelligent AI agents

Our AI Chain Agent Development services help businesses build autonomous AI agents that perform multi-step tasks and workflows. By leveraging chained AI models, APIs, and decision-making algorithms, we create agents that analyze data, execute actions, and deliver results with minimal human intervention. Ideal for process automation, customer support, and intelligent task management, these solutions enhance efficiency, reduce errors, and accelerate business operations.

Use chained agents when a task spans multiple decisions, systems, or iterations (e.g., “find customers with X behavior, draft offers, push to CRM, then email”), where a single prompt isn’t enough and hard-coded workflows are brittle. They shine in semi-structured, branching processes like investigation, reconciliation, or complex escalations.
Good eCommerce use cases: support agents that diagnose an order issue across OMS + courier + payment, operations agents that reconcile payouts and refunds, merchandising agents that analyze sales + inventory + search logs, and marketing agents that pull segments, draft campaigns, and schedule pushes with guardrails.
Architect the agent as a controller service that manages steps: it calls the LLM to decide the next action, then invokes typed tools (API clients, DB queries, search, CRM actions) through a strict schema. Validate inputs/outputs, enforce allowlists and rate limits, and persist each step’s state and logs so you can replay and debug.
For memory, store interaction state and tool results in a structured store (DB or vector index) and feed only a summarized, relevant subset into each new LLM call. Use role-based “working memory” (current task, constraints, decisions so far) plus longer-term profiles; cap context length, auto-summarize, and reset or scope memory per task or session to avoid drift.
A solid human-in-the-loop flow has stages: propose → review → execute. The agent drafts actions (refund, cancellation, bulk price change) with explanation and evidence, routes them into an approval queue UI, and only calls write APIs after explicit human confirmation or policy-based auto-approval for low-risk cases. All approvals and overrides are logged as feedback for future tuning.
Bottlenecks include too many sequential LLM/tool calls, heavy prompts, slow external APIs, and unbounded context growth. Optimize with parallel tool calls where safe, slimmer prompts and schemas, caching intermediate results, using cheaper/smaller models for simple steps, and adding timeouts/fallbacks when tools are slow or flaky.
Treat agents like any other service: add structured logging per step, correlation IDs across calls, metrics (latency, success/error rate per tool), and traces for entire workflows. Build sandbox test harnesses with recorded tool responses, run regression test suites on typical scenarios, and add guardrail tests (red-team prompts, edge cases) before promoting new chains or model versions.

A Chain of Thought- multi agent AI for modern teams

Build multi-agent AI systems that solve, reason, and connect data, tool integration, task mapping, and feedback-driven execution. Human-like, but scalable.

Build agents