An Agent Engineering publication
Reliable agents take more than prompting, software development, or model evaluation. They need requirements, evaluation data, evaluators, experiments, and release decisions that hold up as everything changes. TuneAI explores the emerging Agent Development Lifecycle (ADLC) — practical methods for engineers, product managers, data teams, QA, and leaders.
Published by Traigent.
What makes agents different, and the vocabulary of the discipline.
Say what the agent must do, and how you will know.
Choose models, tools, prompts, and configurations deliberately.
Build representative datasets and evaluators you can trust.
Compare candidates systematically and read the results honestly.
Decide on evidence, then monitor and respond as things change.
A conceptual model, not a strict sequence — requirements, evidence and design keep changing.
Agent engineering borrows from software engineering and machine learning but replaces neither. See what carries over, what is new, and where to start on Monday.
Three labels you will hear for building agents: what eval-driven, spec-driven and experiment-driven work each require, how they combine, and where TDD fits.
A five-level maturity ladder for agents, from 'it worked in the demo' to evidence-based releases, with signs of each level, a next step and a self-check.
Seven roles, seven new challenges. For each one: what changes when you ship an agent, one habit to adopt this week, and one question to ask your team on Monday.
How to turn 'the agent should be helpful' into acceptance criteria you can measure: hard limits, quality targets, trade-offs, thresholds and who decides.
Most AI prototypes die in the gap between an impressive demo and a reliable production system. The secret weapon that bridges that gap? Systematic tuning.
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