Founder & CTO
Tirth Gajjar
Tirth Gajjar is the founder and CTO of Bigcircle. He started the company after several years working with engineering teams in the US and Europe, where he kept running into the same setup: engineers split across three or four accounts, tracked in billable hours, and left on whatever stack the account already ran.
He hired every engineer at Bigcircle himself and trained them the same way, for depth in one or two areas and enough breadth to follow a problem across the stack. He leads the engineering on the agent, retrieval, document intelligence and voice systems the team runs in production.
He writes the guides on this site, mostly about the parts of AI engineering that only show up after launch: retrieval quality degrading over time, agents reporting success on work that is not finished, and how to widen an agent's autonomy one action at a time.
Writing
- How LLM Inference Actually WorksA practical guide to the systems work behind fast, affordable LLM serving.
- Why RAG Systems Fail in ProductionMost RAG failures look like model failures until teams measure retrieval, context assembly, and generation separately.
- The Practical Guide to AI AgentsUseful agents are not magic workers. They are constrained systems that decide, act, verify, and stop.
- How to Derisk Your AI StrategyMost enterprise AI pilots fail for predictable, well-documented reasons. This guide walks through what the evidence says and the decisions that separate the few that reach production value from the rest.
- When Your AI Product Needs Write AccessA practical guide to choosing the first action an AI product can perform inside an authority boundary that engineering, product, and security can review.
- How to Scope AI Agent PermissionsHow to bind one agent action to a principal, task, target, policy, credential, approval, expected state, and recovery path.
- Why AI Agents Report Unfinished Work as DoneA production guide to expected-state contracts, independent verification, idempotency, reconciliation, retry safety, partial failure, and causal audit evidence.
- How to Roll Out AI Agent Autonomy SafelyHow to move one agent action from read-only to bounded execution through explicit authority levels, exit evidence, demotion rules, and shared release ownership.