Workflow mapping & scoping
We document the manual workflow the agent will replace — inputs, steps, decision points, tools accessed, and failure modes. You approve the agent design before any model is called.
AGENTIC AIAGENTIC AIAutonomous AI Agents
Autonomous AI Agents
That Execute
Workflows
We design and build agentic AI systems that autonomously plan, decide, and execute multi-step business workflows — from lead qualification to report generation — with human oversight baked in.
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Agentic AIsystems.
LLM-powered chat is table stakes. Agentic AI goes further — autonomous agents that call APIs, query databases, write reports, and hand off to the next step in a workflow without human input at every stage. We build these systems for Indian enterprises looking to operationalise AI beyond the chatbot.
AGENTIC AI
Autonomous AI agents built for enterprise workflowsWe build agentic AI systems for clients based in Noida, Delhi & NCR, India
Marketing Wise builds agentic AI systems where LLMs act as the reasoning engine — orchestrating tools, APIs, and sub-agents to complete complex business workflows autonomously. Our systems use Claude and OpenAI models with custom tool definitions, retrieval-augmented memory, and deterministic guardrails that keep agents within sanctioned boundaries. We have delivered 15+ agent systems for Indian enterprises since 2023, covering sales research, finance reconciliation, recruitment screening, compliance review, and customer support triage.
The difference between an impressive demo and a production-grade agent is failure recovery, audit logging, and human oversight design. We engineer task decomposition logic that breaks large goals into verifiable steps, tool selection that matches each step to the right API or database, confidence thresholds that trigger human review before irreversible actions, and observability dashboards that let your operations team see every decision the agent makes. Agentic AI that your team can trust — not just watch.
- Multi-agent orchestration with task decomposition
- Tool-calling systems (APIs, databases, file systems)
- RAG memory with vector search and context management
- Human-in-the-loop approval gates for sensitive actions
- Agent observability and audit logging
- Claude and OpenAI model integration with custom prompting
What is an agentic AI system and how is it different from a chatbot?
A chatbot responds to a single user input. An agentic system receives a goal, breaks it into steps, calls tools and APIs, evaluates intermediate results, and completes the goal autonomously — often across dozens of steps without further user input.
Can you build agents that operate on our internal databases and APIs?
Yes. We define tool schemas that give the agent controlled access to your databases, internal APIs, and file systems. Every tool call is logged and can be gated behind human approval workflows for sensitive actions.
How do you prevent agents from making mistakes or going out of bounds?
We implement deterministic guardrails — whitelisted tool sets, action limits, confidence thresholds, and human approval gates for irreversible actions. Agents log every decision step for audit review, and anomaly alerts notify your team if behaviour deviates from expected patterns.
What LLMs do you build with?
We build with Claude (Anthropic) and OpenAI GPT-4 family as primary reasoning models. For cost-sensitive high-volume tasks we route to smaller models like GPT-4o-mini or Claude Haiku. We also support self-hosted Llama 3 via Ollama for air-gapped or on-premise deployments.
Can agents be triggered by external events like emails or webhooks?
Yes. We build event-driven agent systems triggered by email, Slack messages, webhook payloads, database row changes, and scheduled cron jobs. Agents wake, execute their workflow, and post results back to your chosen channel.
How do you handle agent failures mid-workflow?
We build retry logic with exponential backoff for transient failures, checkpoint persistence so agents can resume from the last successful step, and dead-letter queues for tasks that exhaust retries. Your team is notified with a structured failure report, not a silent drop.
Can one agent hand off to another agent in a pipeline?
Yes. We design multi-agent pipelines where a coordinator agent decomposes the goal and routes sub-tasks to specialist agents — a research agent, an extraction agent, a writing agent — and assembles their outputs into a final result.
How do you measure the ROI of an agentic AI system?
We baseline the manual process before build: time per task, headcount involved, error rate, and throughput ceiling. Post-deployment, the same metrics are measured in production. Every system we have delivered has shown measurable reduction in processing time and error rate within 90 days.
Can agents read and process documents like PDFs, invoices, or contracts?
Yes. We build document ingestion pipelines using extraction models (Claude, GPT-4o vision, or Textract) that parse structured and unstructured documents, extract key fields, and feed them into the agent workflow for downstream processing.
What is the typical cost and timeline for an agentic AI system?
Single-agent workflow systems start at ₹12 lakhs and take 10–14 weeks. Multi-agent orchestration systems with complex tool suites and observability infrastructure typically run ₹18–40 lakhs over 16–24 weeks.
180+
Full-stack apps delivered
12+
Years building
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API & platform integrations
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