No-code tools worked for chatbots, not agents. Coding each from scratch took weeks. Microsoft Foundry cut that to a day.
A Korean energy conglomerate spanning petrochemicals, LNG, power, batteries, and renewable energy, four years into an AI transformation program that had produced an internal generative AI portal and no-code automation tools for employees.
Moving from chatbots and no-code automation into Agentic AI exposed a clear gap: low-code tools couldn't handle agents' demands for internal system...
“It used to take two weeks to a month to build an agent from scratch using code, but with Foundry, you can complete an agent within a day simply by writing instructions and clicking the Azure AI Search integration button. The time required for agent development has been dramatically reduced.”
Energy and chemical conglomerate specializing in petroleum refining and batteries.
Enterprise software, cloud infrastructure, and consumer electronics platform.
SK Innovation's Enterprise agent platform is part of this use case:
Related implementations across industries and use cases
Hours of system-hopping to find answers left nothing for analysis. Employees now ask in natural language—instant, structured results.
Manual 3,000-page applications delayed deployments for years. Now, engineers use AI to draft and map them to regulations in seconds.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Building a compliant bot took 512 hours. A unified framework cuts that to 64, launching agents with just 50 lines of code.
Translations needed line-by-line review; meetings ran one-way through interpreters. Now field expertise flows across languages.
Engineers spent weeks manually mapping well sites. Now, geospatial AI evaluates millions of permutations to maximize leasehold coverage.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
No-code tools worked for chatbots, not agents. Coding each from scratch took weeks. Microsoft Foundry cut that to a day.
A Korean energy conglomerate spanning petrochemicals, LNG, power, batteries, and renewable energy, four years into an AI transformation program that had produced an internal generative AI portal and no-code automation tools for employees.
Moving from chatbots and no-code automation into Agentic AI exposed a clear gap: low-code tools couldn't handle agents' demands for internal system...
“It used to take two weeks to a month to build an agent from scratch using code, but with Foundry, you can complete an agent within a day simply by writing instructions and clicking the Azure AI Search integration button. The time required for agent development has been dramatically reduced.”
Energy and chemical conglomerate specializing in petroleum refining and batteries.
Enterprise software, cloud infrastructure, and consumer electronics platform.
SK Innovation's Enterprise agent platform is part of this use case:
Related implementations across industries and use cases
Hours of system-hopping to find answers left nothing for analysis. Employees now ask in natural language—instant, structured results.
Manual 3,000-page applications delayed deployments for years. Now, engineers use AI to draft and map them to regulations in seconds.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Flying blind with agents in critical infrastructure — raw logs the only window. Now production traces feed evaluation before every release.
Building a compliant bot took 512 hours. A unified framework cuts that to 64, launching agents with just 50 lines of code.
Translations needed line-by-line review; meetings ran one-way through interpreters. Now field expertise flows across languages.
Engineers spent weeks manually mapping well sites. Now, geospatial AI evaluates millions of permutations to maximize leasehold coverage.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.