SolutionUpdated March 2026

AI agents that automate multi-step business workflows

Build autonomous AI agents that plan, execute, and optimize complex processes across your existing systems. Not chatbots. Not RPA. Intelligent agents that handle exceptions, use tools, and improve over time.

See Our Approach

Your teams are drowning in repetitive knowledge work

Employees switch between 10+ tools, copy data between systems, answer the same questions repeatedly, and follow rigid processes that should adapt to context. RPA helped automate structured, rule-based tasks, but it breaks when anything changes. The next evolution is AI agents that understand context, make decisions, and handle exceptions intelligently.

5 of 100

AI pilots deliver measurable P&L impact

MIT Research

10+

Tools the average knowledge worker switches between daily

30%

Of knowledge work tasks are repetitive and automatable

80%

Of AI POCs never reach production

How Optivus builds production AI agents

We build AI agents that operate within your existing systems, handling multi-step workflows autonomously while keeping humans in the loop for high-stakes decisions. Our agents use tools (APIs, databases, documents), handle exceptions intelligently, and improve through feedback loops.

01

Identify the workflow

Map your highest-impact repetitive workflow. Define what the agent should do, what tools it needs, and where human oversight is required.

02

Design with guardrails

Architect the agent with clear boundaries: which decisions it can make autonomously, which require human approval, and how it handles edge cases.

03

Build and integrate

Connect the agent to your existing systems (APIs, databases, document stores). Build, test, and iterate in 2-4 weeks with weekly demos.

04

Monitor and expand

Deploy to production with monitoring. Analyze agent performance, gather feedback, and expand to adjacent workflows.

Key capabilities

Multi-step autonomous workflows

Agents that plan and execute sequences of actions across multiple systems, handling branching logic and exceptions.

Tool use and API integration

Agents call APIs, query databases, process documents, and interact with any system that has an interface.

Human-in-the-loop

Configurable escalation for high-stakes decisions. The agent handles routine work; humans handle judgment calls.

Self-improving through feedback

Agent performance improves over time as it learns from corrections, feedback, and outcome data.

Multi-agent coordination

Complex processes handled by multiple specialized agents working together, each handling its part of the workflow.

Production monitoring

Full observability: track agent decisions, tool calls, latency, success rates, and escalation patterns.

Results you can expect

2-4 weeks

From kickoff to production agent deployment

12+ hrs

Saved per team per week on automated workflows

60%

Reduction in manual processing for targeted workflows

Production

Not a demo. Working agents in your systems.

Built with:Claude / GPT-4LangChainLlamaIndexCustom orchestrationRAG pipelinesTool-use frameworksPythonTypeScript

Our AI implementation process

Every engagement follows the same four-phase structure.

01

Scope

Map the workflow, define success criteria, lock deliverables.

02

Build

Weekly working demos. Direct channel with the build team.

03

Ship

Production deployment on your cloud with monitoring.

04

Scale

Optimize on real usage. Expand to adjacent workflows.

Frequently asked questions

A chatbot responds to questions in a conversation. An AI agent autonomously plans and executes multi-step workflows, uses tools (APIs, databases, documents), makes decisions, and takes actions across your systems. Chatbots talk. Agents do.
A focused AI agent targeting one workflow typically takes 2-4 weeks from kickoff to production. More complex multi-agent systems with many integrations take 4-8 weeks. We ship weekly working demos throughout.
Process documents and extract data across systems, automate multi-step approval workflows, answer questions from internal knowledge bases with citations, coordinate between teams and systems, generate reports from multiple data sources, and handle customer interactions with intelligent escalation.
Yes, with proper guardrails. We design every agent with clear boundaries: what it can do autonomously, what requires human approval, and how it handles errors. Full audit trails, monitoring, and rollback capabilities are built in.
Costs depend on complexity, integrations, and scope. A focused single-agent deployment starts in the low lakhs range. Multi-agent systems with complex integrations cost more. We always start with a scoped pilot to prove value before expanding.
RPA follows pre-programmed rules: if X, do Y. It breaks when inputs change or exceptions occur. AI agents understand context, make decisions, and handle variations intelligently. RPA automates structured tasks. AI agents automate knowledge work.

Ready to get started?

Book a 25-minute call. Bring your workflow and we will show you exactly how we would approach it.

See What We Have Built