From Disruptive to Production: Lessons Learned Building Context-Aware AI Agents

The gap between a working demo and a reliable production system is where projects die.

Everyone's excited about AI agents. Demos are flashy—you see Claude or GPT-4 planning a task, calling APIs, and executing multi-step workflows. It feels magical. Then you try to build AI agents that actually work at scale, and reality hits hard.

I've built agents that run in production every day: customer support agents, document processing systems, API testing automation, workflow orchestration. The difference between those that work and those that don't isn't about the model. It's about context.

Most teams discover this too late. They've already shipped something that works in a demo but falls apart under real-world load. The model starts hallucinating. Token costs explode. Response times become unacceptable. And nobody's really sure why.

Here's what I've learned about building context-aware AI agents that actually survive production.

The Context Problem Nobody Talks About

As agents run longer, the amount of information they need to track—chat history, tool outputs, external documents, intermediate reasoning—explodes. This isn't a theoretical problem. It's the primary reason agent projects fail.

When you're prototyping, context management feels optional. You paste in some documentation, add a few examples, and the agent works fine. But production systems aren't single-turn interactions. They're long-running workflows, multi-turn conversations, and complex decision chains.

Context window management has emerged as a critical challenge for AI engineers building production chatbots and agents. As conversations extend across multiple turns and agents process larger documents, the limitations of context windows directly impact application performance, cost, and user experience.

The prevailing "solution" has been to lean on ever-larger context windows in foundation models. But simply giving agents more space to paste text cannot be the single scaling strategy.

Bigger context windows are a crutch. They hide the real problem: you haven't designed your context architecture.

What Changed When We Built for Production

I started with a simple agent that analyzed customer support tickets. It worked great in testing. Then we deployed it to handle 500 tickets a day.

Within a week, two things happened:

Token costs became unsustainable - Each ticket was pulling in the entire conversation history, plus all previous ticket context, plus system prompts. We were spending $2,000/month on a task that should cost $200.
Response quality degraded - The agent started making contradictory decisions. Earlier decisions were being forgotten or contradicted by later context. The model was confused about what had already been decided.

The problem: I was treating context like a dumping ground instead of a resource to architect.

That's when I learned about context engineering. Context engineering is the practice of structuring everything an LLM needs—prompts, memory, tools, data—to make intelligent, autonomous decisions reliably. It moves beyond prompt engineering, focusing on designing the full environment in which an agent operates, not just the questions it receives.

As I explored this deeper, I realized that building production-ready AI agents requires thinking about context as a first-class architectural concern, not an afterthought.

The Architecture That Works

Here's the pattern I've now implemented in multiple production systems:

1. Selective Context Injection

Selective context injection prioritizes the most relevant information for each model invocation rather than including all available context. This approach optimizes context window utilization while maintaining response quality by focusing on information directly relevant to the current user query.

For the support agent, this meant:

Only include relevant previous interactions - Not the entire conversation history, just the last 3 turns and any previous tickets from this customer
Use structured summaries - Instead of dumping raw logs, summarize key decisions and outcomes
Implement dynamic retrieval - Pull relevant documentation only when the agent signals it needs it

// Instead of this:
const context = await getAllTicketHistory(customerId); // 50K tokens

// Do this:
const relevantTickets = await getRecentTickets(customerId, { limit: 5 });
const summary = await summarizeDecisions(customerId, last30Days);
const context = `${relevantTickets}\n${summary}`; // 2K tokens

The result: 96% reduction in token usage, zero quality loss.

2. Memory as a First-Class System

Don't rely on the context window to remember important decisions. Explicitly save them.

These updates create a system that improves agent performance: enable longer conversations by automatically removing stale tool results from context, boost accuracy by saving critical information to memory—and bring that learning across successive agentic sessions. This unlocks new possibilities for long-running agents processing entire codebases, analyzing hundreds of documents, or maintaining extensive tool interaction histories.

For our agents, this looks like:

interface AgentMemory {
  decisionsLog: Array<{
    timestamp: string;
    decision: string;
    reasoning: string;
    impact: string;
  }>;
  systemState: Record<string, any>;
  failurePatterns: Array<{
    pattern: string;
    resolution: string;
  }>;
}

// After each major action, update memory
await updateAgentMemory(agentId, {
  decisionsLog: [...existing, newDecision],
  systemState: currentState,
});

This memory persists across sessions. The next time the agent runs, it starts with institutional knowledge, not a blank slate.

3. Structured Tool Outputs

Never let tools return free-form text. Always use structured schemas. This is one of the core principles I've documented in building reliable AI tools.

const toolSchema = z.object({
  action: z.enum(["approve", "reject", "escalate"]),
  confidence: z.number().min(0).max(1),
  reasoning: z.string(),
  nextSteps: z.array(z.string()).optional(),
  contextRequired: z.array(z.string()).optional(), // What context is needed?
});

const result = await runAgentWithTools(agent, tools, {
  outputSchema: toolSchema,
});

Structured output means:

The model can't hallucinate arbitrary responses
You can validate and route decisions programmatically
Token usage is predictable
Debugging is straightforward

Scalability Lessons

Context Window Awareness

Claude Sonnet 4.5 and Claude Haiku 4.5 feature context awareness, enabling these models to track their remaining context window (i.e., "token budget") throughout a conversation. This enables Claude to execute tasks and manage context more effectively by understanding how much space it has to work.

Use this. Ask the model to check its remaining budget before taking expensive actions.

// In your system prompt:
"Before performing large operations, check your remaining context budget. 
If less than 20% remains, summarize your progress and prepare to hand off."

Strategic Session Management

Avoid running Claude to the limit because response quality declines significantly on tasks requiring broad codebase understanding. Manage constraints by avoiding the last fifth for memory-intensive tasks and starting fresh sessions when approaching limits.

For long-running agents, implement checkpoints:

if (contextUsagePercent > 75) {
  // Summarize progress
  const summary = await agent.summarizeProgress();
  
  // Save to memory
  await memory.append(summary);
  
  // Start fresh session
  agent = createNewSession(memory);
}

The MCP Pattern

A key inflection point came in late 2024, when Anthropic released the Model Context Protocol. The protocol allowed developers to connect large language models to external tools in a standardized way, effectively giving models the ability to act beyond generating text.

Use MCP servers for tool integration, not for hiding context. In this model, MCP's job isn't to abstract reality for the agent; its job is to manage the auth, networking, and security boundaries and then get out of the way. It provides the entry point for the agent, which then uses its scripting and markdown context to do the actual work.

The best MCP servers are thin wrappers around simple data access. They don't hide complexity—they expose it cleanly. For more on orchestrating multiple agents with MCP, check out building production-ready AI agent swarms with MCP orchestration.

Real Numbers

Here's what we saw when we applied these patterns to a production document processing agent:

Metric	Before	After	Change
Tokens per document	45,000	8,500	-81%
Cost per document	$0.68	$0.13	-81%
Latency (p95)	12s	3.2s	-73%
Error rate	8.2%	0.3%	-96%
Monthly cost	$34,000	$6,200	-82%

The latency improvement came from smaller context windows being faster to process. The error rate improvement came from better memory and structured outputs. The cost improvement came from all of it together.

The Architectural Decisions That Matter

If you're building context-aware AI agents for production, focus on these:

Own your context window - Treat it as a resource, not a dumping ground. Architect what goes in and when.
Separate reasoning from execution - The LLM reasons, your code executes. Structured outputs enforce this.
Make memory explicit - Don't rely on context to remember important information. Save it to a memory system that persists across sessions.
Use selective injection - Pull only what's relevant for the current task. Use retrieval, summaries, and dynamic context loading.
Implement checkpoints - Long-running agents need places to pause, summarize, and restart. Plan for this from the beginning.
Monitor ruthlessly - Track token usage, context utilization, error rates, and latency. If you're not measuring it, you can't optimize it.

This is where the real difference between demos and production systems lives. It's not about bigger models or more sophisticated prompts. It's about treating context as something you architect, not something you hope will work.