Build & Test meets Deploy & Manage - experimentation loops plus runtime optimization for production agents.
Traditional software SDLC is not enough for AI agents. Gensten’s Agent Development Lifecycle (ADLC) is the new tech stack SDLC for agentic systems: use-case alignment and KPI definition, agent development with MCP integrations and OpenTelemetry, eval-driven release into a governed catalog, production rollout with multi-agent orchestration, continuous KPI optimization, and real-time monitoring with regulatory alignment. Build & Test and Deploy & Manage loop continuously so agents improve safely after go-live.
New tech-stack SDLC for AI agents - Build & Test (experimentation loop) and Deploy & Manage (runtime optimization loop).
Build & Test
Use case alignment, KPI definition, evaluation framework setup.
Build & Test
Agent development, enterprise system integration through MCP, observability with OpenTelemetry.
Build & Test
Run evals, calculate governance metrics, stage and certify in governed catalog.
Deploy & Manage
Production rollout, multi-agent orchestration, infrastructure scaling.
Deploy & Manage
Performance optimization, continuous improvement against business KPIs.
Deploy & Manage
Real-time monitoring, ongoing audits, regulatory alignment, secure retirement.
Experimentation loop: Test & Release ↔ Code & Build · Runtime optimization: Operate ↔ Deploy
Agents change after release: models drift, retrieval corpora grow, prompts evolve, and tools change. ADLC treats evaluation, observability, and governance as first-class - not optional ops afterthoughts.
Between Test & Release and Code & Build, teams iterate on prompts, tools, and RAG quality. Between Operate and Deploy, production signals trigger scaled rollouts, canaries, and rollbacks - the runtime optimization loop.
ADLC extends MLOps/LLMOps for agents: tool permissions, multi-agent orchestration, RAG quality, human oversight, and business KPI loops - not only model training pipelines.
Yes. We plug eval gates and observability into your CI/CD, Azure DevOps, or GitHub Actions, and align release boards with your change-management process.
Between Test & Release and Code & Build, teams iterate on prompts, tools, RAG quality, and model choices until eval and governance metrics meet release criteria.
Between Operate and Deploy, production signals (latency, cost, failures, KPI drift) trigger canary rollouts, rollbacks, prompt/index updates, and infrastructure scaling.
Always Plan: align the use case, define KPIs, and set the evaluation framework. Skipping Plan leads to agents that look impressive in demos but fail business outcomes.
We run golden datasets for accuracy, groundedness, safety, latency, and cost; calculate governance metrics; then stage and certify the agent in a governed catalog before production.
Monitor includes ongoing audits, versioned prompts/models/tools, access logs, and secure retirement paths so auditors can see what ran, when, and under which policy.
A lightweight Gen AI / agent CoE helps, but ADLC can start with one product squad. We help define roles: product owner, agent engineer, data steward, and risk reviewer.
Continuous small improvements are preferred. Material changes to tools, models, or corpora go through Test & Release gates; hotfixes can use accelerated paths with post-release review.
In Code & Build: MCP integrates enterprise systems as tools; OpenTelemetry instruments traces for prompts, retrieval, and tool calls so Operate/Monitor has real production signal.
Design, build, and operate enterprise AI agents that plan, use tools, orchestrate workflows, and deliver measurable business outcomes.
Production Retrieval-Augmented Generation: chunking, embeddings, vector search, reranking, citations, and agent-ready knowledge retrieval.
Build, evaluate, and operate enterprise agents and models on Microsoft Azure AI Foundry - with governed catalogs, tools, and Foundry IQ intelligence patterns.
Build, adapt, and operate LLMs for enterprise workloads - prompt systems, fine-tuning, evaluation, hosting, and cost/performance tradeoffs.
Talk to Gensten about ADLC, RAG, LLM building, Foundry IQ, Fabric IQ, and OneLake - scoped to your KPIs and compliance needs.