AI Agent Development Services We Offer in Bangalore
Bangalore agent engineering has a specific bar. Postman built the agent UX standard for developer-facing tools. Hasura wired agentic workflows into data APIs. Atlan ships agents inside data catalog and governance flows. Razorpay runs agents inside collections, dispute resolution, and risk. Swiggy and Flipkart run customer-service agents at hundreds of millions of conversations per quarter. Cred runs agents inside premium customer support and lifecycle marketing. We build to that bar. Customer-service agents in English, Hindi, Kannada, Tamil, Telugu, and Hinglish that integrate with Freshdesk, Zendesk, Salesforce Service Cloud, and the proprietary CRMs the Bangalore consumer-internet stack uses. Internal copilot agents for the SRE, security, and finance teams inside Walmart Labs, Microsoft IDC, and Google Bangalore Global Capability Centres, with strict on-shore data residency and the typical GCC IT governance controls. Sarvam, Krutrim, and AI4Bharat-based Indic agents for clients targeting Tier-2 and Tier-3 India.
Our AI Agent Development Development Process
Bangalore engagements start with a Digital Personal Data Protection Act 2023 mapping, an RBI Digital Lending Guidelines review where the client is an NBFC or lending service provider, an evaluation harness definition (because Bangalore product teams demand offline eval before any model promotion), and an Indic language scope decision. Sprints run two-week IST cycles with daily 10:30 AM standups, a Friday demo with the client product manager, and a fortnightly steering meeting. Every agent ships with an offline eval pack, red-team logs covering prompt injection, jailbreak, and data exfiltration patterns, an explicit human-in-the-loop policy for high-stakes flows (collections, lending decisions, account closure), and a documented rollback plan. Bangalore product teams do not accept demoware, so we ship to the eval-driven cadence local engineering leadership expects from peer Postman, Hasura, and Atlan releases.
Process Discovery
1-2 WeeksWe sit with the people doing the work in {city} and record the real process — including the exceptions they handle by instinct, which are exactly what kill naive automations.
Tool Surface Design
1-2 WeeksEvery system the agent touches gets a typed, permission-scoped tool with its own rate limit and rollback path. The agent gets a narrow set of verbs, never raw admin access.
Build & Evaluate
3-6 WeeksThe agent is built alongside its evaluation suite from day one, using real tasks from your business with verified outcomes. Every change is scored before it ships.
Shadow Mode
2-3 WeeksThe agent runs against live traffic but commits nothing. We compare its proposed actions to what your team actually did and tune until agreement is high enough to trust.
Staged Autonomy & Run
OngoingAutonomy is released by risk band — reversible actions first, irreversible ones keeping a permanent human gate. Then we monitor completion rate, escalations, latency and spend.
Technologies We Use for AI Agent Development
Bangalore agent stacks default to AWS ap-south-1 Mumbai or asia-south2 Delhi for storage and inference, with Bedrock for Anthropic Claude and Meta Llama, Azure South India and Central India for OpenAI when client EA contracts exist, GCP asia-south1 Mumbai for Gemini, and self-hosted Sarvam OpenHathi 7B, Krutrim base, Llama 3, Mistral, or Qwen when DPDPA cross-border restrictions or RBI sensitivity classifications rule out hosted frontier models. Orchestration runs on LangGraph, CrewAI, LangChain, or in-house frameworks. Indic capability uses Sarvam Embed for retrieval, AI4Bharat IndicXEmb embeddings for code-mixed Hinglish and Kanglish text, IndicTrans2 for translation legs, and IndicBART for summarisation in Indian languages. Observability is Langfuse, Arize, or Datadog. Evals run on Promptfoo, Ragas, and DeepEval, with the offline-first cadence Bangalore product teams expect.
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