Stop Giving AI Agents More Context Than They Need

Why oversized prompts, outdated documentation, and unclear task boundaries can make AI agents less useful—and how I'm designing smaller, more reliable task handoffs.

I've been reconsidering how I use AI agents in my development workflow, particularly Grok Bots. My original approach was to create specialized assistants that could help coordinate projects, analyze requirements, prepare documentation, and support the work happening across John Liter.

The idea was to reduce repetitive work and make development easier to manage. But as I explored the possibilities, I started thinking about increasingly complicated agent configurations, larger collections of reference documents, and workflows where multiple assistants could participate in the same task.

Eventually, I exhausted my available Grok usage. I can't attribute that consumption to oversized prompts or any particular agent without detailed usage records, but the experience gave me a reason to reconsider how I was structuring the work.

One of the problems I want to address is context management.

When we configure an AI agent, it's tempting to give it everything: the complete project history, architecture documentation, coding standards, business information, previous conversations, and instructions for every situation it might encounter.

That sounds thorough, but it can introduce unnecessary complexity.

An agent doesn't necessarily need to understand an entire organization to complete one well-defined task. It needs accurate information about the task, relevant constraints, and a clear description of the expected result.

I'm now approaching agent context more like an application interface: provide the information required for the operation, validate assumptions, and avoid unnecessary dependencies.

Here's how I'm designing that approach.

1. Why More Context Isn't Always Better

Large language models operate within context windows. The input tokens can include system instructions, conversation history, retrieved documents, tool results, and the user's current request. Generated output also consumes tokens and may be subject to separate limits.

Providing more information can improve a response when that information is relevant. But adding unrelated material doesn't guarantee better reasoning.

Imagine asking an agent to prepare a development brief for a website contact form.

You provide:

• The entire website repository documentation.

• Three months of development notes.

• Several outdated architecture proposals.

• Every business service description.

• Previous conversations about unrelated applications.

• The actual contact form requirement.

The agent now has a substantial amount of information to process, but only a small portion may be necessary to complete the task.

There are several potential problems.

First, unnecessary input consumes context capacity and may increase usage, depending on the provider's accounting model.

Second, older documentation may conflict with the current implementation.

Third, the agent may give irrelevant details more attention than they deserve.

Finally, reviewing and correcting the output may take longer because the response incorporates assumptions that were never part of the requested work.

This doesn't mean short prompts are always better. Some tasks genuinely require substantial context.

The objective is to provide sufficient, authoritative context, not the maximum amount available.

2. Separate Global Rules From Task-Specific Information

One change I'm planning for my Grok Bots is to maintain a small set of shared operating principles and separate those from individual agent responsibilities.

The shared instructions should establish rules that apply to every task.

For example:

# Shared Agent Rules

1. Use supplied evidence as the source of truth.
2. Distinguish implemented features from planned work.
3. Do not invent test results or project milestones.
4. Identify missing information when it affects accuracy.
5. Do not execute work requiring human authorization.
6. Stay within the assigned task scope.

These rules establish consistent expectations without requiring every bot to receive the entire history of my development environment.

Each specialized agent can then have a separate role definition.

For example:

# Technical Planning Agent

Purpose:
Convert approved requirements into implementation briefs.

Allowed:
- Analyze supplied requirements
- Identify dependencies
- Prepare acceptance criteria
- Document unresolved questions

Not Allowed:
- Modify application code
- Execute commands
- Deploy applications
- Approve its own recommendations

Output:
A structured Markdown development brief.

This is an illustrative design, not a configuration I've already verified in production.

There's also an important security distinction: written instructions are not the same as enforced permissions. If an agent shouldn't execute commands or access production systems, the surrounding tool configuration must restrict those capabilities.

The goal is to establish clear responsibilities while minimizing unnecessary context.

3. Give the Agent a Task Packet, Not an Entire Repository History

For individual tasks, I'm moving toward the idea of a task packet.

A task packet contains the minimum information necessary to complete a particular assignment.

For a technical planning request, it might look like this:

task_id: WEB-014

agent_role: technical-planning

objective: >
  Prepare an implementation brief for
  server-side contact form validation.

current_state:
  source: approved-project-status
  notes:
    - Contact form UI exists.
    - Production submission behavior is unverified.

requirements:
  - Validate required fields.
  - Reject malformed input.
  - Preserve existing user-facing errors.

constraints:
  - No production changes.
  - No new dependencies without approval.
  - No unrelated route changes.

deliverable:
  format: markdown
  sections:
    - Objective
    - Verified Current State
    - Proposed Work
    - Acceptance Criteria
    - Open Questions

authorization:
  planning: approved
  implementation: not_authorized

This example is hypothetical and doesn't describe a verified change to my website.

The important feature is the structure.

The objective is explicit. The current state is separated from requested behavior. Constraints are documented. The output format is defined. Authorization is limited to planning.

The agent doesn't need to infer what it should do from a long conversation.

It also doesn't need to decide whether it has permission to modify the application.

That boundary is already established.

4. Use Current Status Documents to Prevent Outdated Assumptions

One of the most frustrating problems with AI-assisted development is receiving a recommendation based on an architecture that no longer exists.

This becomes more likely when a project changes frequently.

I've been reorganizing my development environment and reconsidering how different applications fit into the John Liter Ecosystem. Some earlier plans have been revised, while other ideas remain unimplemented.

If I give an agent several months of notes without explaining which decisions are current, it may treat an abandoned proposal as an active requirement.

I want each project to have a concise current-status document.

For example:

# Project Status

Last reviewed: YYYY-MM-DD
Status owner: Project owner

## Implemented
- Public inventory catalog
- Vehicle detail pages

## In Progress
- Dealer inventory management

## Planned
- Customer notification workflow

## Not Authorized
- Production deployment

## Source of Truth
- Current requirements document
- Approved architecture decisions
- Verified test reports

The date and source references matter because they help establish whether the information is still relevant.

However, a document labeled "current" isn't automatically correct. It still needs to be maintained, and claims about implementation or testing should be supported by actual evidence.

The purpose is to reduce ambiguity, not create another document that everyone assumes must be accurate.

5. Define the Output Before Sending the Task

Another way to reduce unnecessary agent interaction is to specify the deliverable before submitting the request.

Vague instructions tend to produce responses that require clarification.

For example:

Review my website and tell me what needs improvement.

That could produce anything from design feedback to a complete architecture proposal.

A more useful request would be:

Review the supplied website requirements and current implementation notes. Identify up to five discrepancies that affect the contact form. For each discrepancy, explain the supporting evidence, expected behavior, and recommended next action. Do not propose unrelated redesign work.

The second instruction establishes scope, evidence requirements, and output limits.

It also makes the response easier to evaluate.

I can determine whether the agent identified relevant issues, supported its conclusions, and stayed within the assignment.

This doesn't guarantee correctness, but it gives me a clearer standard for reviewing the output.

6. Don't Automatically Pass Full Agent Responses to the Next Agent

When several agents participate in a workflow, context can grow quickly.

Suppose a planning agent produces a detailed report. A review agent receives the entire report plus the original task and generates another lengthy response. A coordination agent then receives both outputs and prepares a summary.

Each stage may repeat information from earlier stages.

The result can be a growing chain of prompts and responses that becomes increasingly difficult to maintain.

A better approach is to define what information actually needs to cross each handoff.

For example:

{
  "task_id": "WEB-014",
  "status": "planning_complete",
  "objective": "Prepare contact form validation brief",
  "verified_findings": [
    "The supplied status document does not verify production submission behavior."
  ],
  "open_questions": [
    "Which fields are mandatory?",
    "What is the approved retention policy?"
  ],
  "next_action": "human_review",
  "authorized_execution": false
}

This structured response is illustrative.

Instead of forwarding an entire conversation, the next stage receives a concise record of the information needed to continue.

For implementation work, I would still retain links or references to the original requirements and evidence. A summary should not replace authoritative source material when precise technical details are required.

This is particularly important for security-sensitive decisions, where losing a qualification or constraint during summarization can create real problems.

7. A Simple Python Validator for Task Packets

Before passing structured task packets to an AI agent, I can validate their basic shape using ordinary code.

This doesn't require another language model.

The following example uses only Python's standard library and validates a JSON task packet.

import json
from pathlib import Path

REQUIRED_FIELDS = {
    "task_id",
    "agent_role",
    "objective",
    "requirements",
    "deliverable",
    "authorization",
}

def validate_task_packet(packet: dict) -> list[str]:
    errors = []

    missing = REQUIRED_FIELDS - packet.keys()

    if missing:
        errors.append(
            f"Missing fields: {', '.join(sorted(missing))}"
        )

    if not isinstance(packet.get("objective"), str):
        errors.append("objective must be a string")

    if not isinstance(packet.get("requirements"), list):
        errors.append("requirements must be a list")

    if not isinstance(packet.get("authorization"), dict):
        errors.append("authorization must be an object")

    return errors


def main():
    path = Path("task.json")
    packet = json.loads(path.read_text(encoding="utf-8"))

    if not isinstance(packet, dict):
        raise ValueError("Task packet must be a JSON object")

    errors = validate_task_packet(packet)

    if errors:
        for error in errors:
            print(f"ERROR: {error}")
        raise SystemExit(1)

    print("Task packet structure is valid.")


if __name__ == "__main__":
    main()

This validator only checks a few structural requirements. It doesn't establish that the task is accurate, safe, complete, or authorized.

For a real workflow, I would extend it to validate field types, allowed agent roles, task identifiers, output schemas, and authorization rules.

The important principle is that predictable validation belongs in deterministic code.

I don't need an AI agent to decide whether a required JSON field exists.

I can use Python for that and reserve AI assistance for the parts of the task that involve interpretation or judgment.

8. Measure the Cost of Context, Not Just the Number of Requests

When evaluating an AI workflow, request count alone doesn't tell the full story.

Two tasks might each involve one model request but have very different context requirements.

One might contain a short instruction and a few paragraphs of documentation. Another might include extensive source material, multiple tool results, and a long conversation history.

Depending on the provider, model, and pricing or allowance structure, those differences may affect usage significantly.

For my revised workflow, I want to track several practical measurements:

Measurement

Why it matters

Input context size

Helps identify oversized task packets

Number of model interactions

Reveals repeated or unnecessary requests

Output length

Shows whether responses exceed the task's needs

Human correction time

Measures the cost of unreliable output

Task acceptance rate

Indicates whether the result satisfies requirements

Platform-reported usage

Helps evaluate actual resource consumption

I haven't completed those measurements for the revised Grok Bot configuration.

Until I have reliable data, I won't claim that a particular task packet size or instruction format produces a specific percentage of savings.

What I can do is establish a structure that makes comparison possible.

For example, I can run representative tasks using a broad-context prompt and a focused task packet, then compare the quality of the results, the correction effort, and any usage information the platform exposes.

The goal isn't to minimize tokens at any cost.

It's to determine how much context is necessary to produce reliable work.

9. Keep Context Boundaries Separate From Security Boundaries

There is one important limitation to this entire approach.

Providing less information to an agent can reduce unnecessary exposure, but prompt design alone doesn't create a secure system.

If an agent has access to a privileged connector, its permissions must be configured appropriately.

If it receives external documents, those documents may contain misleading information or instructions intended to redirect the model's behavior.

If it handles sensitive data, the surrounding workflow needs appropriate access controls, retention practices, and review procedures.

A task packet should identify which information the agent needs, but the system should also enforce which information and actions are available.

For my Grok Bots, I'm deliberately avoiding unnecessary coding, image-generation, and production-execution responsibilities.

That simplifies the intended trust boundary.

The bots can help prepare work without needing direct control over the systems where that work will eventually be performed.

10. What I'm Changing in My Own Workflow

I'm currently preparing to rebuild my Grok Bots around smaller, clearly defined responsibilities.

Each bot will have shared operating rules, a specific role definition, and a predictable output format.

Tasks will be submitted with relevant context rather than entire collections of unrelated project material.

Current project status documents will help distinguish implemented features from planned work.

And when a task requires coding, the bot will prepare a structured handoff for me rather than attempting to modify the application itself.

This is still a planned restructuring. I haven't completed the new configuration or measured its results.

But the direction is much clearer than my original approach.

I want agents that are easy to understand, easy to evaluate, and limited to work that genuinely benefits from AI assistance.

Conclusion

Context management is becoming an important part of AI-assisted development.

Giving an agent more information may sometimes improve its output, but excessive, outdated, or irrelevant context can introduce unnecessary complexity and make results harder to evaluate.

For my own workflow, I'm moving toward concise shared rules, specialized agent contracts, current project documentation, and structured task packets.

I'm also keeping deterministic validation in ordinary code and reserving AI for tasks that benefit from language understanding and analysis.

The goal isn't to give an agent everything I know about a project. It's to give it everything it needs to complete the task correctly—and nothing that unnecessarily complicates the work.

I'm interested in how other developers handle this.

Do you provide your AI agents with extensive project documentation, or have you found that smaller, task-specific context produces more reliable results?

Comments

[Junfeng Zhou]

Task packets make sense. Putting the done check in the packet (how the agent proves it finished) tends to matter more than how much background it gets.