If you've built a Context Layer for your AI agents, you've solved the architecture problem. But architecture is only half the battle.

The real challenge isn't having a context system. It's operating one.

How do you measure if your context is actually good? How do you debug when your agent hallucinates despite having "all the right context"? How do you maintain context quality as your knowledge base grows from 100 documents to 100,000?

This is where Context Engineering comes in: the emerging discipline of designing, monitoring, and maintaining context pipelines for production AI systems. Just like DevOps transformed how we deploy software and MLOps transformed how we train models, Context Engineering is transforming how we build reliable agents.

• Context engineering is the new meta-skill - Writing prompts is table stakes; curating what fills the context window is what separates demos from production
• Models suffer from "context rot" - Research shows LLM accuracy degrades as input length increases, even on simple tasks
• Context fails in four distinct ways - Poisoning, distraction, confusion, and clash each require different fixes
• Bigger windows don't solve the problem - A Databricks study found accuracy drops around 32K tokens, long before million-token limits
• Context engineering is a system, not a string - It's dynamic, format-aware, and runs before every LLM call
• Real-time web data keeps agents current - Firecrawl gives agents access to live web data so they don't rely on stale training knowledge

Let's start with a definition. Andrej Karpathy put it simply:

People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window with just the right information for the next step.

Tobi Lutke, CEO of Shopify, describes it as "the art of providing all the context for the task to be plausibly solvable by the LLM."

Here's my working definition:

Context Engineering is the discipline of designing dynamic systems that provide the right information and tools, in the right format, at the right time, to give an LLM everything it needs to accomplish a task.

Notice the key words:

• Dynamic systems - Context isn't a static prompt template. It's the output of a system that runs before every LLM call.
• Right information - Not all information. The minimal set of high-signal tokens.
• Right format - How you present information matters. A concise summary beats a raw data dump.
• Right time - Context is assembled just-in-time for the immediate task.

What fills your context window? Everything the model sees before generating a response:

Context engineering is the craft of curating all of these for every single inference call.

This is the question everyone asks. Here's the simplest way to think about it:

Prompt engineering is what you do inside the context window. It's crafting instructions for a single task: "You are an expert X. Do Y like Z."

Context engineering is how you decide what fills the window. It's designing the entire mental world the model operates in: memory, retrieval, tools, history, and yes, the prompt.

Prompt engineering is a subset of context engineering, not the other way around.

You can engineer a killer prompt. But if it gets buried behind 6K tokens of irrelevant chat history or poorly formatted retrieved docs? Game over.

Prompt engineering gets you the first good output. Context engineering makes sure the 1,000th output is still good.

The arrival of million-token context windows felt transformative. "Just dump everything in," they said. "Gemini has 2 million tokens."

This is lazy engineering. And the research proves it doesn't work.

The foundational Stanford research on "lost in the middle" proved that LLM performance degrades significantly when relevant information is placed in the middle of long contexts. Models perform best when key information appears at the very beginning or end of the input. This isn't just about total length. It's about position.

This is why context engineering obsesses over structure, not just content. Where you place information matters as much as what information you include.

Chroma Research published a comprehensive study in July 2025 testing 18 LLMs, including Claude 4, GPT-4.1, Gemini 2.5, and Qwen3. Their findings are stark:

Models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows.

Even on trivially simple tasks (like replicating a sequence of repeated words) models failed as context grew. This isn't a reasoning failure. It's a fundamental limitation of attention mechanisms.

Performance degrades non-uniformly as context length increases, even on simple retrieval tasks.

The study found that:

• Lower similarity needle-question pairs degraded faster with length
• Distractors have non-uniform impact that amplifies with context size
• Even haystack structure affects performance (shuffled content performed better than logically structured content)

A Databricks study found that model correctness begins dropping around 32,000 tokens for Llama 3.1 405b, and earlier for smaller models.

If models start to misbehave long before their context windows are filled, what's the point of super large context windows? Summarization and fact retrieval. If you're not doing those specific tasks, be wary of your chosen model's distraction ceiling.

The Gemini 2.5 technical report documented this while building a Pokémon-playing agent:

As the context grew significantly beyond 100k tokens, the agent showed a tendency toward favoring repeating actions from its vast history rather than synthesizing novel plans.

Instead of developing new strategies, the agent became fixated on repeating past actions from its extensive history.

Research on long context utilization found something counterintuitive: adding more relevant context can actually hurt performance. Models struggle to synthesize information spread across many locations. The optimal context isn't "all the relevant information." It's the minimum relevant information, structured for attention.

A study on multi-turn conversations took benchmark prompts and "sharded" them across multiple conversation turns, mimicking how real conversations unfold. The results were devastating: an average performance drop of 39% across all tested models, including frontier reasoning models.

Why? The assembled context contains early attempts by the model to answer before it has all the information. These incorrect answers remain in context and poison subsequent reasoning.

Based on research from Drew Breunig and the Gemini team, context fails in four distinct ways. Each requires a different fix.

What it is: A hallucination or error makes it into the context, where it's repeatedly referenced.

Real example: The Gemini Pokémon agent would occasionally hallucinate about the game state, poisoning its "goals" section. The agent then developed nonsensical strategies pursuing impossible objectives for a long time.

The fix: Context validation and quarantine. Isolate different context types in separate threads. Validate information before it enters long-term memory. Start fresh threads when you detect poisoning.

What it is: Context grows so large that the model over-focuses on accumulated history, neglecting what it learned during training.

Real example: Models start repeating past actions instead of developing new strategies. They lean heavily on their context history rather than reasoning from first principles.

The fix: Context summarization. Compress accumulated information into shorter summaries that preserve important details while removing redundant history. This is what compaction techniques achieve.

What it is: Superfluous content in the context influences the response, even when irrelevant.

Real example: The Berkeley Function-Calling Leaderboard shows that every model performs worse when given more than one tool. Models will occasionally call tools that aren't relevant to the task.

A recent study gave a quantized Llama 3.1 8b a query with 46 tools. It failed, even within the 16k context window. With only 19 tools, it succeeded.

The fix: Tool loadout management using RAG. Research shows that applying RAG to tool descriptions and keeping selections under 30 tools gives 3x better tool selection accuracy.

What it is: Different parts of your context directly contradict each other.

Real example: When connecting to MCP tools you didn't create, their descriptions might clash with the rest of your prompt. Or information gathered in stages disagrees with earlier assumptions.

The fix: Context pruning and offloading. Remove outdated or conflicting information as new details arrive. Use techniques like Anthropic's "think" tool that give models a separate workspace to process information without cluttering the main context.

Now that we understand the problems, let's talk solutions. These techniques come from production systems at companies like Anthropic, Google, and the teams building Claude Code.

The most effective approach for agent prompts isn't free-form text. It's a structured "prompt spec" that clearly defines:

• Objective: What the agent should accomplish
• Constraints: Hard boundaries (budget limits, forbidden actions, style requirements)
• Tools available: What capabilities the agent can use
• Output contract: The exact format expected back

This structure helps both you and the model. You're forced to think through edge cases. The model gets unambiguous boundaries instead of vague instructions it might misinterpret.

When your agent needs to produce structured data, define a schema upfront. JSON contracts prevent the model from improvising field names or formats that break downstream code.

from pydantic import BaseModel, Field
 
class ExtractedEntity(BaseModel):
    name: str = Field(description="Entity name")
    type: str = Field(description="Entity type: person, company, or location")
    confidence: float = Field(ge=0, le=1, description="Extraction confidence score")

This isn't just about parsing convenience. It's context engineering. The schema becomes part of the context, constraining the model's output space and reducing hallucination.

The guiding principle: find the minimum set of tokens that maximizes the likelihood of your desired outcome.

This means ruthlessly curating what enters the context. AI data preparation is half the battle:

• Don't dump entire docs - Retrieve only the relevant sections
• Summarize when possible - A 100-token summary often beats 10,000 raw tokens
• Remove stale information - Old conversation turns might be hurting, not helping
• Use tool result clearing - Once a tool has been called deep in history, clear the raw result

Instead of stuffing all possible information into the prompt upfront, give agents tools to retrieve context on demand. This is the ReAct (Reasoning + Acting) pattern: the model reasons about what information it needs, acts to retrieve it, then reasons again with the new context.

This shifts context engineering from "predict everything the model might need" to "give the model ways to get what it needs." It's more robust because you're not guessing what will be relevant.

The trade-off: more LLM calls, higher latency. But for complex tasks, it dramatically improves accuracy.

Rather than pre-processing all data upfront, maintain lightweight identifiers (file paths, stored queries, URLs) and load data dynamically at runtime.

Claude Code uses this approach for complex data analysis:

• The model writes targeted queries
• Stores results
• Uses commands like head and tail to analyze large volumes
• Never loads full data objects into context

This mirrors human cognition. We don't memorize entire corpuses but use organization systems to retrieve what we need on demand. It's also why CLIs are better for agents than GUIs: they provide structured, predictable outputs that fit cleanly into context.

As we covered in the Context Layer article, rigorously separate your context:

Decision Context (Static) - Goes at the beginning of your prompt:

• Coding standards
• Business rules
• API specifications
• Brand guidelines

This enables prompt caching that can save up to 90% of token costs.

Operational Context (Dynamic) - Goes at the end of your prompt:

• Current user state
• Error logs
• Session variables
• Real-time data

Placing dynamic context last leverages recency bias so the model attends to it strongly.

For tasks spanning tens of minutes to hours, you'll exceed the context window. Compaction distills the contents while preserving critical details.

Example from Claude Code:

1. Pass message history to the model for summarization
2. Preserve architectural decisions, unresolved bugs, implementation details
3. Discard redundant tool outputs
4. Continue with compressed context plus five most recently accessed files

The art is knowing what to keep versus discard. Overly aggressive compaction loses subtle but critical context.

Your agents need real-time web context. Documentation changes. APIs deprecate methods. Policies update. Your agent's training data is already stale.

This is where Firecrawl fits into your context engineering stack. It gives your agents access to real-time web data, turning the adversarial web into structured, fresh context that keeps your agents current. (See how it compares to other scraping tools, or explore how OpenClaw uses Firecrawl for real-time agent context.)

The modern web is hostile to simple GET requests:

• Client-side rendering - Content is injected by React 700ms after load
• Anti-bot defenses - Cloudflare and Akamai block scrapers
• Navigation traps - Infinite scroll, shadow DOMs, iframe injections

Firecrawl abstracts this chaos. Here's how you create a self-healing context pipeline using the /agent endpoint:

from firecrawl import Firecrawl
from pydantic import BaseModel, Field
from typing import List, Optional
 
app = Firecrawl(api_key="fc-YOUR_API_KEY")
 
# Define schema for structured context extraction
class APIChange(BaseModel):
    method: str = Field(description="The API method name")
    status: str = Field(description="Current status: deprecated, removed, or changed")
    replacement: Optional[str] = Field(None, description="The replacement method if any")
 
class APIChangesSchema(BaseModel):
    changes: List[APIChange] = Field(description="List of API changes")
 
# Agent searches, navigates, and extracts without needing specific URLs
result = app.agent(
    prompt="Find any deprecated or changed API methods from the Stripe changelog",
    schema=APIChangesSchema,
    model="spark-1-mini"
)
 
# This becomes your agent's decision context
print(result.data)

Set this as a cron job, and your context layer becomes self-healing. When an API deprecates a method:

1. Without Firecrawl: Agent uses the old method from training data. Code crashes. You debug for hours.
2. With Firecrawl: The cron job detects the changelog. Updates decision context. Agent generates correct code on the first try.

A warning on web-sourced context: Any time you pull context from the open web, you're opening a vector for prompt injection. Malicious pages can embed instructions that hijack your agent. Always sanitize web-sourced content and consider isolating it from high-privilege operations.

For research agents, competitive intelligence, or keeping RAG pipelines fresh, this pattern is essential.

Recent academic research studying AGENTS.md files in open-source projects identified five distinct styles for writing context instructions:

The research found no established best practice yet. Teams are still experimenting. But the most effective context files combine multiple styles, using prohibitive statements to set hard boundaries and conditional statements to encode situational logic.

How do you know if your context engineering is working? Here are the metrics that matter:

The ultimate metric. Are your agents completing tasks correctly? Track this across context lengths to identify your distraction ceiling.

Measure how often agents generate information not grounded in the provided context. Contradiction detection can automate this.

Of the tokens you provide, how many actually influence the output? Low utilization suggests you're providing noise.

Context isn't free. More tokens = slower responses and higher costs. Track these against task success to find the optimal balance.

For dynamic context, measure the age of information. Stale context causes context drift, where agents use outdated information.

We are entering the era of Context Engineering. For the last two years, we obsessed over models. "Is GPT-5 better than Claude 3.5?" "What's the MMLU score?"

Now, all the frontier models are good enough. The differences between them are marginal compared to the difference between "an agent with the right context" and "an agent without it."

If you're building agents in 2026, you are a data engineer, an architect of context. Stop obsessing over model weights you can't control. Start obsessing over your context pipeline, which you can.

Here's your checklist:

1. Audit your current context - What's actually entering your prompts? Is it high-signal?
2. Identify your failure modes - Which of the four failure types are hitting you?
3. Implement dynamic retrieval - Move from "dump everything" to just-in-time loading
4. Separate static and dynamic context - Enable prompt caching, leverage recency bias
5. Automate ingestion - Use Firecrawl to keep context fresh
6. Measure and iterate - Track success rates across context configurations

The agents that win won't be the ones with the biggest context windows. They'll be the ones with the most carefully engineered context.

Try Firecrawl for free, test it in the Playground, or get inspired by 10 AI projects you can build today.