Team collaborating around a conference table representing how agents must allocate limited context capacity across multiple information sources — Photo by Walls.io on Unsplash

What Is a Context Window in AI Agents?

Q: What is a context window in AI?

A context window is the maximum number of tokens — chunks of text — that an AI language model can process in a single call. Everything the model uses to generate a response, including the system prompt, conversation history, retrieved documents, tool results, and the current user message, must fit within this limit.

Q: How large are context windows in current AI models?

As of early 2026, context windows range widely. GPT-4o supports 128K tokens, Claude 3.5 Sonnet supports 200K tokens, and Gemini 1.5 Pro supports up to 1 million tokens. Larger windows enable longer reasoning chains and more retrieved context but typically increase latency and cost.

Q: How do AI agents manage context window limits?

Agents use several strategies: summarizing earlier conversation turns, selectively retrieving only the most relevant documents via RAG, compressing tool outputs, maintaining a separate memory system for long-term information, and using prompt-chaining to break long tasks into shorter sub-tasks with their own context windows.

Quick Definition#

A context window is the maximum amount of text — measured in tokens — that a large language model can process in a single inference call. A token is roughly equivalent to three to four characters of English text, so 100K tokens corresponds to approximately 75,000 words, or about a 250-page book. Every piece of information the model considers when generating a response must fit within this limit: the system prompt, all prior conversation turns, any retrieved documents, tool call results, and the current user input.

For AI agents, the context window is effectively working memory. It defines the scope of what the agent knows and can reason about at any given moment. Understanding how context windows work — and how to manage them — is essential for building reliable multi-step agents.

Start with What Are AI Agents? for foundational concepts, and explore the AI Agents Glossary to learn how context windows interact with memory, retrieval, and planning.

What Goes Into a Context Window#

In a typical agent inference call, the context window contains several categories of content:

System prompt: Instructions that define the agent's persona, capabilities, constraints, and behavioral rules. These are set by the developer and persist across most calls.

Conversation history: Prior turns of the dialogue between user and agent, including previous tool calls and their results. As a task progresses, this section grows with each step.

Retrieved documents: Content pulled from a vector database or search system via retrieval-augmented generation. Each retrieved chunk consumes tokens proportional to its length.

Tool results: The outputs of tool calls the agent has already made — API responses, database query results, file contents, web search results. Complex tools can return verbose outputs.

Current input: The user's most recent message or the current task instruction.

Each of these sections competes for space within the fixed token budget. A poorly managed context window leads to truncation, where the model silently drops earlier content to fit within its limit, often discarding information that was relevant to the task.

Context Window Sizes Across Models (2026)#

| Model | Context Window | Notes | |-------|---------------|-------| | GPT-4o | 128K tokens | Strong reasoning, widely supported | | Claude 3.5 Sonnet | 200K tokens | High capacity, strong instruction following | | Claude 3 Opus | 200K tokens | Largest Anthropic reasoning window | | Gemini 1.5 Pro | 1M tokens | Experimental 2M available in some tiers | | Llama 3.1 405B | 128K tokens | Open-source, self-hosted option | | Mistral Large | 128K tokens | European data residency option |

Larger context windows increase the volume of information an agent can reason over in a single call, which reduces the need for complex context management strategies. However, larger windows also increase latency (the model must process more tokens) and cost (most providers charge per token). The right window size depends on the specific task and the token budget available.

Why Context Window Management Matters for Agents#

Single-turn interactions — a user asks a question, the model answers — rarely bump into context limits. The challenge emerges in multi-step agent workflows where the context accumulates over many tool calls and reasoning steps.

Consider a research agent tasked with synthesizing information from twenty documents. Each document may be ten to twenty pages. Even with a 200K context window, the agent cannot load all twenty documents simultaneously. It must decide what to include and what to leave out at each reasoning step.

Context mismanagement produces several failure modes:

Silent truncation: Earlier task context is silently dropped as new content is added, causing the agent to "forget" instructions or prior decisions
Instruction dilution: A long context causes the model to give less weight to the original task instruction relative to later content
Hallucination under pressure: When relevant context has been truncated, the model may generate plausible-sounding but fabricated content to fill gaps
Cost overruns: Naive context management that includes all available information on every call can multiply inference costs dramatically

For teams tracking agent reliability, context management failure is a leading cause of degraded performance on long-horizon tasks.

Team collaborating around a conference table representing how agents must allocate limited context capacity across multiple information sources

Strategies for Managing Context Windows#

Selective Retrieval (RAG)#

Rather than loading all available knowledge into the context window, agents use retrieval-augmented generation to fetch only the most relevant documents for the current step. A vector similarity search retrieves the top-K chunks most semantically similar to the current query, keeping the context focused and within budget.

This is the most widely used context management strategy in production agents. It pairs well with Vector Databases and Embeddings for efficient retrieval.

Conversation Summarization#

As conversation history grows, agents can periodically summarize earlier turns into a compact representation and replace the raw history with that summary. The summary preserves key facts and decisions while consuming far fewer tokens than the full transcript.

The challenge is that summarization is lossy — nuances in earlier turns may be lost. Teams should use summarization selectively, keeping recent turns in raw form and summarizing only older history.

Sliding Window#

A sliding window approach keeps only the most recent N turns of conversation in the active context, discarding earlier turns. This is the simplest strategy but the most aggressive in what it discards. It works well for tasks where only recent context is relevant (a live customer service conversation) and poorly for tasks with dependencies on earlier steps (a multi-day research project).

External Memory Systems#

For information that must persist across many turns or sessions, agents can store it in an AI Agent Memory system — a separate key-value store, document database, or vector database — and retrieve it on demand. This moves long-term information out of the context window entirely and into a queryable external store.

External memory enables agent behavior that effectively transcends any single context window limit. The agent maintains a compact active context and pulls from external memory when relevant, combining the benefits of large knowledge bases with efficient token usage.

Prompt Chaining and Task Decomposition#

Prompt chaining breaks a complex task into a sequence of simpler sub-tasks, each with its own focused context window. The output of one call becomes the structured input to the next, rather than requiring all task context in a single call. Task decomposition applies similar logic at a higher level, planning which subtasks to execute before beginning execution.

These strategies are especially effective for long-horizon tasks that exceed even large context windows.

Context Windows and Agent Planning#

Context window constraints directly influence how agents approach Agent Planning. A well-designed agent must reason about what information it currently needs versus what it can retrieve later, and structure its plan accordingly.

Agents with sophisticated planning capabilities — especially those using Chain-of-Thought reasoning — tend to produce better plans when given sufficient context budget to reason through the problem. Truncating the planning phase to save tokens often results in worse downstream execution.

Practical Implications for Builders#

When designing agents, consider context budget as a first-class resource, similar to memory in systems programming:

Budget your context explicitly: Allocate token limits to each section (system prompt, history, retrieved documents, tool results) and enforce them programmatically.
Monitor context usage in production: Track how full the context window is on each call. Calls approaching the limit are risk candidates for truncation errors.
Test at realistic context lengths: Agents that perform well in short demo conversations often degrade at production context lengths. Test with realistic task depths.
Choose models based on context needs: If your agent routinely needs to reason over long documents or extended histories, select a model whose context window fits the use case without constant management overhead.

For production deployment guidance, see How to Deploy AI Agents in Your Company and Understanding AI Agent Architecture.

Frequently Asked Questions#