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01AI Development AgencyCustom AI agents, copilots & automation02AI ServicesAgentic AI services and platforms03Custom MVP DevelopmentMVP and product development04UI/UX DesignProduct design & branding05Software Staff AugmentationScale your engineering team06ServicesView all our services
01TechCrunchEnterprise blogging platform02State AffairsAI-powered policy intelligence03WyndMobility platform MVP04EquooMental health gaming app05ImmaginnTalent showcase platform06AjenticAI-powered CRM system07RainbowEdTech learning platform
Blog
About Us
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Insights

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Insights, tutorials, and updates on software development, AI automation, and technology trends from our engineering team.

All PostsAI & AutomationAI & MLDevOpsIndustry SolutionsMarketingOtherSoftware DevelopmentTeam & HiringTechnologyWeb Development
Buyer team reviews an Agentic AI Due Diligence checklist beside evaluation harness results, production traces, token chart, and contract terms labels15 MIN
AI & ML

Agentic AI due diligence: Expert Questions for 2026

Unlock effective agentic AI due diligence with our essential checklist and 22 focused questions to assess potential firms thoroughly.

Ankit Kumar BaralAnkit Kumar Baral2026.08.25
Split diagram with Pilot on the left, Production on the right, and an 80% progress bar stopped between labels for workflow complexity, data quality, integrations, security, observability, evaluation, 14 MIN
AI & ML

AI Agent Production: Bridging the Gap in 2026

Discover the reasons behind stalled AI agent pilots and learn practical strategies to ensure successful AI deployment in enterprise settings.

Naresh HRNaresh HR2026.08.25
Side-by-side diagram comparing deterministic workflow steps with an AI agent loop, including labels for fixed states, predictable outputs, dynamic tool selection, retries, cost, and debugging difficul14 MIN
AI & ML

Deterministic Workflows vs AI Agents: When Simple Wins in 2026

Discover when to avoid AI agents in deterministic workflows, and what more effective alternatives exist for your systems. Simplify and enhance your processes!

Suvam SwainSuvam Swain2026.08.24
Architecture diagram with User, LLM App, MCP Server security layers, internal systems, blocked threat arrows, and a retrieved content is data never instruction label16 MIN
AI & ML

MCP security guide: Protecting Internal Systems from LLM Risks

Explore our MCP security guide to learn how to expose your internal systems to LLMs safely and minimize risks like data exfiltration.

Ankit Kumar BaralAnkit Kumar Baral2026.08.24
Architecture diagram of an AI agent run with planner, retrieval, LLM, tool calls, shared correlation ID, step-by-step timeline, and failure labels including malformed tool output and retry loop15 MIN
AI & ML

AI Observability: Debugging Agentic Failures in Production 2023

Learn how AI observability can help you debug agentic failures without stack traces. Uncover logs, metrics, and best practices for effective agent monitoring.

Naresh HRNaresh HR2026.08.24
Flowchart with Success Criteria, Representative Test Dataset, AI Evaluation Harness, Agent Logic, Regression Tests, and CI/CD Gate connected in sequence14 MIN
AI & ML

AI evals: Building Reliability Before Agent Development

Discover why AI evals should precede agent development. Define success criteria, build test datasets, and automate evaluations for reliable performance.

Naresh HRNaresh HR2026.08.23
Wide diagram of an AI agent connected to Short-Term Memory, Episodic Memory, and Semantic Memory with labels for active context, task history, facts, storage options, scalability, cost, and retrieval 12 MIN
AI & ML

Agent Memory Architecture: Layered Design Strategies for 2026

Discover how to differentiate short-term, episodic, and semantic memory in agent memory architecture for enhanced AI performance and cost savings.

Naresh HRNaresh HR2026.08.21
Architecture diagram with Orchestrator, Planner Agent, Worker Agent, Tool API, Checkpoint Store, and nine labeled failure points including delegation loops, retry storms, and unreproducible runs13 MIN
Technology

Multi-Agent Failure Modes: Diagnosing Issues in Production

Explore a field guide to diagnosing and fixing common multi-agent production failures, from delegation loops to weak evaluations.

Suvam SwainSuvam Swain2026.08.21
Decision diagram with four labeled columns for LangGraph, CrewAI, AutoGen, and Custom Orchestration, comparing control flow, state, HITL, debugging, streaming, deployment, maturity, and failure recove15 MIN
AI & ML

LangGraph vs CrewAI: A Fair Guide for Workflow Success in 2026

Stuck choosing between LangGraph and CrewAI? Our guide compares control flow, state management, and more to help you choose the right framework.

Ankit Kumar BaralAnkit Kumar Baral2026.08.21
Three-column matrix comparing MCP, function calling, and AI plugins across architecture, use cases, security boundary, scalability, implementation effort, and maintenance15 MIN
AI & ML

MCP vs function calling: Strategic Insights for 2026

Explore the strategic choices between MCP, function calling, and AI plugins. Understand architecture, trade-offs, and implementation efforts for your next project.

Naresh HRNaresh HR2026.08.20
Executive dashboard with a pilot funnel and labeled cost bars totaling an estimated $100k to $285k before production21 MIN
AI & ML

AI Pilot Failure: The Hidden Costs of Stalled Production

Explore the hidden costs of AI pilot failures, understand critical decisions, and discover how to avoid budget blowouts.

Ankit Kumar BaralAnkit Kumar Baral2026.08.20
Regional benchmark graphic listing US, UK/Europe, UAE, Eastern Europe, LatAm, India commodity, and India boutique hourly engineering rate ranges with labels for seniority, IP risk, timezone fit, accou16 MIN
Technology

AI engineering rates 2026: Regional Benchmarking Insights

Discover how AI engineering rates 2026 vary regionally and why total cost plus rework outweighs hourly rates for effective budgeting.

Suvam SwainSuvam Swain2026.08.20
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