Context Engineering in Codex: How Code Agents Manage Long Conversations and Large Repositories

The effectiveness of an artificial intelligence agent focused on programming depends not only on its reasoning capability but also on how the right information is selected, preserved, and presented during the execution of a task. When an agent receives a seemingly simple mission—such as adding JWT authentication to an existing FastAPI application—the success of the operation is closely linked to what we call Context Engineering. If the AI starts modifying off-limits files, forgets architectural rules, or repeats searches already performed, the problem is rarely a lack of model intelligence. In the vast majority of cases, it is a serious failure in the management of the information environment.

What is Context Engineering and how did we get here?

In the early days of adopting Large Language Models (LLMs) for programming, the industry's focus was almost exclusively on Prompt Engineering. The logic was simple: all it took was formulating the perfect instruction ("Add authentication to my FastAPI application using JWT") to get the desired code. However, as real-world projects proved to be vast and interconnected, this approach became insufficient.

Context Engineering, as defined by experts like Bharani Subramaniam and Birgitta Böckeler from Thoughtworks, answers a much broader question: what does the model need to know about the existing architecture, previous decisions, tool outputs, and constraints to execute the task correctly?

Historically, we saw an explosion of context configuration tools at the beginning of 2026. Tools like Claude Code began to lead innovations in this space, but OpenAI Codex quickly structured its own Agent Loop. OpenAI technical staff developer Michael Bolin recently detailed how the Codex CLI architecture orchestrates interactions between users, LLMs, and tools, making it clear that the focus has shifted from "pure instruction" to "curation of the information environment."

Context Windows and the Token Budget

To understand how Codex handles long-term development, it is essential to understand the physical constraint of transformer-based models: the context window. Every piece of information provided to the model is converted into tokens.

A context limit is not synonymous with unlimited working memory. Consider a hypothetical budget of 128,000 tokens. In an enterprise application, this space is quickly devoured by:

• System instructions and base rules (e.g., the AGENTS.md file).
• Directory structure and database schemas (FastAPI files, Pydantic, Alembic).
• Conversation history between the developer and the AI.
• Tool outputs (test results, error logs).
• Reasoning space and the model's own output generation.

If an entire repository is injected into the context window, the model will run out of space to "think" or generate the response. More importantly, tokens do not have the same value. One thousand tokens describing security requirements are infinitely more useful than ten thousand tokens of generic log utilities. Larger context windows, therefore, do not automatically solve the problem of continuous development; they only create a larger space that can also be filled with noise.

Repository Exploration and Context Compression

Faced with the token limit, efficient agents rely on targeted repository exploration rather than blind code ingestion. In Codex, the request flow maps which modules contain the business logic, where data is validated, and which files should be read next.

When the conversation approaches the context limit, Context Compression comes into play. Version 0.157.1 of the Codex CLI, for example, performs history compression automatically or via the /compact command. What the system does is preserve the fixed prefix (instructions, MCP tools) and summarize or truncate intermediate message history.

In multi-hour tasks, the recommended engineering practice is to create a chain of short "threads," linked by a handoff file and validated by automated tests. This ensures that constraints established in the third interaction are not forgotten by the fortieth.

Impact on the Sector and the Latin American Market

The transition from prompt engineering to context engineering has profound impacts on the software development sector. For companies, it means the ability to integrate new developers into large repositories much more agilely. The time required to map unknown modules or understand complex data flows can be drastically reduced.

In the Latin American market, the impact is twofold, driven by cost efficiency. Since LLMs charge per token (both input and output) and costs are in US dollars, injecting entire repositories in an unoptimized way drains the budgets of tech startups in the region. Mastering Context Engineering allows teams in Brazil, Mexico, and Colombia to use agents like Codex in a financially viable way. By using structured summaries and aggressive compression, Latin startups can achieve the same level of top-tier technical assistance while spending a fraction of the cost in tokens.

Furthermore, the standardization of contexts through files like AGENTS.md helps outsourcing teams in Latin America quickly align with the rigorous architectures of North American and European clients, minimizing rework.

What to expect next

The future of context engineering for code agents points toward increasingly autonomous and predictive systems in managing their own memory. We can expect the following trends in the short to medium term:

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  Dynamic Prompt Caching: As highlighted by OpenAI, the use of prompt caching requires exact prefix matching. Future versions of Codex and response APIs will further optimize caching to retain immutable components of the repository, drastically reducing latency and cost per conversation turn.
• Long-Term Semantic Memory: Agents will begin using embedded vector databases invisibly to the user, allowing the AI to retrieve architectural decisions made weeks earlier, even after multiple cleansings of the active context.
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  Context Observability Tools: Dashboards will emerge in IDEs focused solely on showing what the model "is seeing" at the moment (similar to /status or codex debug prompt-input commands), helping technical leads audit the quality of information passed to the AI before blaming the model for hallucinations.

Frequently Asked Questions

1. What differentiates Prompt Engineering from Context Engineering? While Prompt Engineering focuses on how the instruction or request is formulated (the exact words used to ask the model something), Context Engineering focuses on the ecosystem of data that accompanies that request. It determines which files, logs, conversation histories, and documentation should be packaged and sent so that the AI has the complete and correct scenario.

2. Why does the code agent sometimes modify files it shouldn't? This usually occurs due to the loss or dilution of context. If the restrictive instruction (e.g., "do not alter v1 routes") was given very early in the conversation and the token window filled up with extensive error logs, the model loses this constraint during compression or simply gives it less weight due to the noise of recent irrelevant information.

3. What is the /compact command and when should I use it? /compact is a mechanism (present in the Codex CLI, for example) to manage the token limit. It forces the agent to summarize the conversation history and clean up temporary data (such as very long tool outputs), keeping only the essential prefix (system instructions, tools, and current code state). It should be used in long sessions to prevent the context window from being exhausted and the AI from beginning to fail due to a lack of free "memory."