Last week I was reviewing the architecture of an AI agent platform designed to automate video generation.
The concept was solid.
A researcher agent gathered information, planning agents created the structure, writing agents generated content, and downstream agents transformed everything into outputs ready for video production.
On the surface, it worked remarkably well.
Every agent produced structured JSON.
The workflow completed successfully.
The final output looked convincing.
If I had only evaluated the demo, I probably would have approved the architecture.
Then I started looking at how the agents communicated with each other.
That’s where the real problems appeared.
The Architecture Looked Like This
Instead of giving each agent only the information it actually needed, almost every agent received nearly the entire execution context.
Research.
Conversation history.
Intermediate outputs.
Previous reasoning.
Generated content.
Planning documents.
Everything.
The assumption was simple:
More context means smarter agents.
For a prototype, that’s often true.
For production, it becomes one of the biggest architectural liabilities.
Why It Works During Development
Small AI systems hide architectural mistakes.
Imagine you have:
- 5 users
- Short conversations
- One execution at a time
- Small documents
- Limited history
Giving every agent the full context feels harmless.
The model performs well.
Developers move faster because they don’t need to think about orchestration or state management.
It creates the illusion that the architecture is scalable.
Unfortunately, it usually isn’t.
What Happens in Production
As usage grows, the problems multiply.
1. Token Costs Increase Exponentially
Every agent repeatedly receives information that has nothing to do with its current task.
Instead of processing:
Research Summary
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the agent receives:
Research
Planning
Draft
Feedback
Previous outputs
Conversation history
System logs
Metadata
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Now imagine that happening across ten or twenty agents.
Your token consumption grows far faster than your user base.
2. Performance Becomes Unpredictable
Large contexts increase latency.
Every request requires:
- Larger prompts
- More serialization
- Higher inference time
- Larger responses
One slow agent delays every downstream agent.
Eventually the orchestration pipeline becomes the bottleneck.
3. Agents Lose Focus
LLMs are surprisingly good at extracting useful information.
They’re also surprisingly good at using information they shouldn’t.
If an agent receives unrelated context, there’s always a chance it influences the output.
A formatting agent shouldn’t make editorial decisions.
A title generator shouldn’t rewrite research.
A quality reviewer shouldn’t accidentally inherit draft instructions.
Giving agents excessive context blurs their responsibilities.
4. Debugging Becomes Nearly Impossible
Imagine asking:
Why did this agent generate this output?
If every execution contains hundreds of unrelated context objects, finding the answer becomes difficult.
Was it:
- today’s research?
- yesterday’s execution?
- another agent’s notes?
- conversation history?
- hidden metadata?
Without clear boundaries, root cause analysis becomes guesswork.
The Bigger Issue: Context Isolation
The problem wasn’t only “too much context.”
The architecture had no strong concept of task isolation.
Multiple agents operated on shared execution data without strict ownership.
That introduces risks such as:
- Context bleeding between tasks
- Incorrect assumptions
- Hard-to-reproduce bugs
- Inconsistent outputs
- Reduced traceability
This isn’t always obvious during development.
It becomes painfully obvious under production load.
Every Agent Should Have One Job
One principle has consistently worked well in distributed systems:
Components should receive only what they need to perform their responsibility.
AI agents are no different.
Instead of this:
Entire Workflow Context
↓
Every Agent
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Think like this:
Task
↓
Context Builder
↓
Relevant Context
↓
Agent
↓
Structured Output
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The context builder becomes responsible for assembling exactly what the agent requires—nothing more.
Task IDs Matter More Than People Think
One thing I missed in this architecture was proper task-level identification.
Every execution should have unique identifiers such as:
- Workflow ID
- Execution ID
- Task ID
- Parent Task ID
- Correlation ID
These IDs make it possible to:
- trace every decision
- replay executions
- audit outputs
- isolate failures
- prevent accidental context mixing
Without them, large multi-agent systems become difficult to reason about.
Context Should Be Retrieved, Not Broadcast
Instead of broadcasting the entire execution state to every agent, use retrieval.
When an agent starts work, ask:
- What task is this?
- What information is required?
- Which previous outputs are relevant?
- Which documents should be retrieved?
- Which constraints apply?
Build a task-specific context.
Everything else stays outside the prompt.
The result is:
- Lower cost
- Faster execution
- Better reasoning
- Easier debugging
- Stronger security
- More predictable behavior
Enterprise AI Isn’t About Bigger Prompts
Many discussions around AI focus on:
- Better prompts
- Better models
- Better structured outputs
Those things matter.
But once systems reach production scale, orchestration becomes more important than prompting.
The quality of an AI platform depends less on how intelligent each agent is and more on how clearly responsibilities are separated.
Final Thoughts
The system I reviewed wasn’t failing because of the LLM.
It wasn’t failing because of the prompts.
It wasn’t failing because of structured outputs.
Its biggest weakness was architectural.
As an industry, we’re spending enormous effort making agents smarter.
We should spend just as much effort making them smaller, more focused, and better isolated.
In distributed software, good architecture comes from clear boundaries.
The same principle applies to multi-agent AI systems.
As these systems move from demos to enterprise production, context management will become one of the defining factors separating reliable platforms from impressive prototypes.
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