AI Agent Development Company

The shift is about write access. A support chatbot that hallucinates gives a bad answer. An agent with an API key issues a refund. That single difference reorders the entire engineering problem: you now need permissions, idempotency keys, rollback paths, spend limits and a log of every decision. Search behaviour reflects the shift — interest in AI agents for business is up sharply year on year while chatbot interest falls — but most vendors have renamed the same retrieval demo.

Ask them what happens when the model calls the same tool forty times in a loop, or when a retrieved document contains instructions the agent obeys. The useful question is not which model you use. It is what the agent is allowed to do, who approves it, and how you find out when it goes wrong at 2am. That is systems work, and it is where most agent projects quietly stall.

• Chatbots answer; agents act — tool calls, database writes, API calls, payments, ticket transitions
• Every action needs an owner: least-privilege credentials scoped per agent, never a shared admin key
• Irreversible actions get human approval gates; reversible ones get automatic rollback
• Success is measured in completed tasks and cost per run, not conversation quality scores

Wallet infrastructure teaches habits that transfer directly. Nonce management becomes idempotency keys on tool calls. Multisig thresholds become human-in-the-loop approvals above a rupee value you set. Immutable transaction logs become structured traces of every prompt, tool argument and result, retained for audit. Deterministic state machines become explicit agent graphs instead of a model looping until it feels finished. None of this is theoretical for us — exchange systems fail loudly and publicly, and that shaped how we design.

We also know when not to build an agent. If the workflow is deterministic, a queue and a cron job will beat an LLM on cost, latency and reliability every time, and we will tell you that in the first call rather than after the invoice. We work with Indian companies and international clients, in your stack, in your cloud, with your compliance constraints intact.

• Security-first design carried over from smart contract and exchange work: least privilege, signed actions, replay protection
• Full traceability — every run reconstructable from stored traces for audit, dispute or RCA
• Honest scoping: we say no to agent projects that should be plain automation
• Delivery in your environment — AWS, GCP, Azure or on-prem — with code and prompts owned by you

The default is a single agent with a tight tool set and an explicit state graph in LangGraph, checkpointed to Postgres so a crashed run resumes instead of restarting. We reach for multi-agent designs in three cases: parallel fan-out over many documents or records; separation of privilege, where a researcher agent reads but only a narrow executor agent writes; and adversarial review, where a second model checks the first one's output against source data before anything commits.

Handoffs are typed and validated with Pydantic, not free text passed between prompts. Long-running work goes to a durable orchestrator — Temporal or a queue-backed worker — so an agent can wait three days for an approval without holding a process open. Shared memory sits in Postgres with pgvector, scoped per tenant. Agents that need your internal knowledge get retrieval built properly, with citations and evaluation, rather than a vector database bolted on and hoped for.

• Single agent by default; multi-agent for parallelism, privilege separation or verification
• Explicit state graphs with checkpointing — resumable runs, no lost work on restart
• Typed, validated handoffs between agents instead of prompt-to-prompt telephone
• Durable orchestration for workflows that span hours or days, including human approval waits

Runaway loops get hard step budgets and per-run token ceilings that halt and alert rather than burn spend silently. Hallucinated tool arguments get schema validation before execution, so a malformed invoice ID is rejected at the boundary instead of writing garbage to your ERP. Context overflow gets summarisation checkpoints and retrieval scoped to the current sub-task, not the whole history. Non-idempotent retries get idempotency keys, so a timeout followed by a retry never creates a duplicate order — the exact discipline that keeps a double-broadcast transaction from double-spending.

Prompt injection gets treated as untrusted input: retrieved documents and user content never carry tool-calling authority, and privileged actions require a separate confirmed path. On top of that sits observability — OpenTelemetry traces, Langfuse for run-level inspection, and an eval suite that runs against a golden dataset on every prompt or model change, so upgrades are measured rather than guessed.

• Step budgets, token ceilings and per-run cost caps with alerting before spend escalates
• Schema-validated tool arguments and idempotency keys on every write path
• Retrieved content is untrusted by default — no tool authority from documents or user text
• Regression evals on a golden dataset gate every prompt, tool and model change
• Structured traces and dashboards so failures are diagnosable, not mysterious