Build AI agents that autonomously plan, reason, and execute complex business tasks. From single-agent workflows to multi-agent orchestration—with the guardrails needed for production deployment.
Agentic AI refers to AI systems that can autonomously take actions to achieve goals, rather than just generating text responses. An AI agent uses an LLM as its reasoning engine, combined with tools (APIs, databases, browsers) and memory, to plan and execute multi-step tasks with minimal human intervention. The key difference from traditional AI: agents act, not just advise.
Automate complex workflows that require reasoning, not just rule-following. Data processing, reporting, coordination.
AI agents for lead research, outreach personalization, CRM updates, and pipeline management.
Agents that resolve issues autonomously, escalate appropriately, and take actions in your systems.
Automated research workflows that gather, synthesize, and report on complex topics.
Build AI assistants that actually do tasks in your internal systems, not just answer questions.
Replace brittle RPA with intelligent agents that handle edge cases and adapt to changes.
We build agents that are capable enough to be useful and constrained enough to be safe. Production-grade from day one.
Map your business processes, identify automation opportunities, and define agent capabilities and boundaries.
Design the agent system: single vs multi-agent, tool integrations, state management, and human-in-the-loop points.
Implement with proper guardrails, testing, monitoring, and gradual rollout to production.
Deep-dive articles on building production AI agent systems, from fundamentals to deployment.
Understanding AI agents: reasoning, planning, tool use, and the components that make autonomous systems possible.
Read articleWhen to use one powerful agent vs. coordinating multiple specialized agents for complex tasks.
Read articleHow agents interact with APIs, databases, web browsers, and external systems to take real-world actions.
Read articleMapping business processes to agent workflows with decision points, human-in-the-loop, and error handling.
Read articleManaging agent execution, maintaining context across steps, and coordinating complex multi-step tasks.
Read articleImplementing constraints, validation, human oversight, and fail-safes for production agent systems.
Read articleA production AI agent system requires these components working together.
LLM that plans tasks, makes decisions, and determines next actions
APIs, databases, browsers, and external systems the agent can use
Short-term context and long-term memory for multi-step workflows
Constraints, validation, human oversight, and safety mechanisms
When to use AI agents vs. traditional automation approaches.
We build with proven frameworks and LLMs, choosing the right tools for your use case.
Agent orchestration and stateful workflows
Foundation models for agent reasoning
When off-the-shelf doesn't fit your needs
APIs, MCP servers, browser automation
Based in Bangalore, we help enterprises across India and globally build AI agent systems that actually work in production—not just demos.
We build agents with guardrails, monitoring, and failure handling from day one. No prototype-to-production surprises.
We map your actual business processes to agent workflows, identifying where AI automation adds real value vs. where simpler solutions work.
Agent systems improve with data. We set up evaluation frameworks and feedback loops to continuously enhance performance.
Share your project details and we'll get back to you within 24 hours with a free consultation—no commitment required.
Boolean and Beyond
825/90, 13th Cross, 3rd Main
Mahalaxmi Layout, Bengaluru - 560086
590, Diwan Bahadur Rd
Near Savitha Hall, R.S. Puram
Coimbatore, Tamil Nadu 641002