Beyond LLMs: Why Scalable Enterprise AI Adoption Depends on Agent Logic
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huggingface.co
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IBM's mainframe code assistant illustrates how preparing information before a model answers can reduce its workload. The App Insights AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → analyzes application code in advance and stores structured findings in a database, letting it retrieve relevant information for a question. IBM reports slightly better application understanding with roughly one-thirtieth the token consumption of its model-only baseline on the legacy systems tested. The useful mechanism is the combination of program analysis and targeted retrieval, which gives the LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition → less material to work through. IBM's account
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
