Agentic workloads push LLMs beyond simple chat completions. A software agent must maintain state across multiple turns, parse unstructured environment data, invoke external tools, and reason about long sequences of previous actions. These requirements expose the weaknesses of standard inference setups. Context windows fill quickly, latency compounds across loops, and token-based billing turns long tool histories into unpredictable costs. Optimizing for agents means rethinking both the model architecture you deploy and the economics of the platform serving it.
The Architecture of Agentic LLMs
An agent is not a single prompt. It is a control loop that repeatedly calls an LLM to generate a thought, select a tool, execute the tool, and feed the result back into context. The most common pattern is ReAct, where reasoning traces and action calls are interleaved in the same context window. This design places unique pressure on the inference layer. Each loop appends new tokens to the input, so a conversation that starts at 2,000 tokens can grow to 20,000 tokens or more within a few minutes.
To keep loops coherent, your system prompt must be precise. It should define available tools with JSON schemas, specify output formats, and set guardrails for halting conditions. The model itself must support function calling natively, or you must parse tool invocations from raw text, which adds fragility and latency.
Context Management Strategies
Long contexts are inevitable in agentic systems, but not every token deserves equal attention. Effective agents use a hierarchy of context management techniques. Sliding window truncation drops the oldest turns when the buffer exceeds a threshold, though this risks losing critical state. Summarization compresses early conversation history into a condensed memory block, preserving intent while freeing token space. For retrieval-heavy agents, injecting only the top-k relevant chunks from a vector store keeps the working set dense.
Model selection also determines how much context you can keep in play. Oxlo.ai hosts models with extended context windows that are purpose-built for these scenarios. DeepSeek V4 Flash supports a 1M context, making it suitable for agents that must ingest entire codebases or lengthy document collections in a single session. Kimi K
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