Agentic systems

Long-horizon reasoning and state tracking

Claude's latest models handle long-horizon reasoning tasks with strong state tracking. Claude maintains orientation across extended sessions by focusing on incremental progress, making steady advances on a few things at a time rather than attempting everything at once. This capability especially emerges over multiple context windows or task iterations, where Claude can work on a complex task, save the state, and continue with a fresh context window.

Context awareness and multiwindow workflows

Claude Sonnet 5, Claude Sonnet 4.6, Claude Sonnet 4.5, and Claude Haiku 4.5 feature context awareness, enabling the model to track its remaining context window (that is, its "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.

Managing context limits:

If you are using Claude in an agent harness that compacts context or allows saving context to external files (like in Claude Code), consider adding this information to your prompt so Claude can behave accordingly. Otherwise, Claude may sometimes naturally try to wrap up work as it approaches the context limit. The following is an example prompt:

Your context window will be automatically compacted as it approaches its limit, allowing
you to continue working indefinitely from where you left off. Therefore, do not stop
tasks early due to token budget concerns. As you approach your token budget limit, save
your current progress and state to memory before the context window refreshes. Always be
as persistent and autonomous as possible and complete tasks fully, even if the end of
your budget is approaching. Never artificially stop any task early regardless of the
context remaining.

The memory tool pairs well with context awareness for managing context transitions.

Workflows across multiple context windows

For tasks spanning multiple context windows:

1. Use a different prompt for the very first context window: Use the first context window to set up a framework (write tests, create setup scripts), then use future context windows to iterate on a todo-list.

2. Have the model write tests in a structured format: Ask Claude to create tests before starting work and keep track of them in a structured format (for example, tests.json). This leads to better long-term ability to iterate. Remind Claude of the importance of tests: "It is unacceptable to remove or edit tests because this could lead to missing or buggy functionality."

3. Set up quality of life tools: Encourage Claude to create setup scripts (for example, init.sh) to gracefully start servers, run test suites, and linters. This prevents repeated work when continuing from a fresh context window.

4. Starting fresh versus compacting: When a context window is cleared, consider starting with a brand new context window rather than using compaction. Claude's latest models are extremely effective at discovering state from the local filesystem. In some cases, you may want to take advantage of this over compaction. Be prescriptive about how it should start:

   • "Call pwd; you can only read and write files in this directory."
   • "Review progress.txt, tests.json, and the git logs."
   • "Manually run through a fundamental integration test before moving on to implementing new features."

5. Provide verification tools: As the length of autonomous tasks grows, Claude needs to verify correctness without continuous human feedback. Tools that let Claude verify UI work are helpful, such as the computer use tool, the browser use tool, or a browser automation MCP server.

6. Encourage complete usage of context: Prompt Claude to efficiently complete components before moving on:

This is a very long task, so it may be beneficial to plan out your work clearly. It's
encouraged to spend your entire output context working on the task - just make sure you
don't run out of context with significant uncommitted work. Continue working
systematically until you have completed this task.

State management best practices

• Use structured formats for state data: When tracking structured information (like test results or task status), use JSON or other structured formats to help Claude understand schema requirements.

• Use unstructured text for progress notes: Freeform progress notes work well for tracking general progress and context.

• Use git for state tracking: Git provides a log of what's been done and checkpoints that can be restored. Claude's latest models perform especially well in using git to track state across multiple sessions.

• Emphasize incremental progress: Explicitly ask Claude to keep track of its progress and focus on incremental work.

  {
    "tests": [
      { "id": 1, "name": "authentication_flow", "status": "passing" },
      { "id": 2, "name": "user_management", "status": "failing" },
      { "id": 3, "name": "api_endpoints", "status": "not_started" }
    ],
    "total": 200,
    "passing": 150,
    "failing": 25,
    "not_started": 25
  }

  // Progress notes (progress.txt)
  Session 3 progress:
  - Fixed authentication token validation
  - Updated user model to handle edge cases
  - Next: investigate user_management test failures (test #2)
  - Note: Do not remove tests as this could lead to missing functionality

Balancing autonomy and safety

Without guidance, Claude Opus 4.6 may take actions that are difficult to reverse or affect shared systems, such as deleting files, force-pushing, or posting to external services. If you want Claude Opus 4.6 to confirm before taking potentially risky actions, add guidance to your prompt:

Consider the reversibility and potential impact of your actions. You are encouraged to
take local, reversible actions like editing files or running tests, but for actions that
are hard to reverse, affect shared systems, or could be destructive, ask the user before
proceeding.

Examples of actions that warrant confirmation:
- Destructive operations: deleting files or branches, dropping database tables, rm -rf
- Hard to reverse operations: git push --force, git reset --hard, amending published commits
- Operations visible to others: pushing code, commenting on PRs/issues, sending
messages, modifying shared infrastructure

When encountering obstacles, do not use destructive actions as a shortcut. For example,
don't bypass safety checks (e.g. --no-verify) or discard unfamiliar files that may be
in-progress work.

Research and information gathering

Claude's latest models can find and synthesize information from multiple sources effectively. For optimal research results:

1. Provide clear success criteria: Define what constitutes a successful answer to your research question.

2. Encourage source verification: Ask Claude to verify information across multiple sources.

3. For complex research tasks, use a structured approach:

Search for this information in a structured way. As you gather data, develop several
competing hypotheses. Track your confidence levels in your progress notes to improve
calibration. Regularly self-critique your approach and plan. Update a hypothesis tree or
research notes file to persist information and provide transparency. Break down this
complex research task systematically.

This structured approach helps Claude work through large corpora methodically and iteratively critique its findings.

Subagent orchestration

Claude's latest models orchestrate subagents natively. These models can recognize when tasks would benefit from delegating work to specialized subagents and do so proactively without requiring explicit instruction.

To take advantage of this behavior:

1. Ensure well-defined subagent tools: Have subagent tools available and described in tool definitions.
2. Let Claude orchestrate naturally: Claude will delegate appropriately without explicit instruction.
3. Watch for overuse: Claude Opus 4.6 has a strong predilection for subagents and may spawn them in situations where a simpler, direct approach would suffice. For example, the model may spawn subagents for code exploration when a direct grep call is faster and sufficient. Claude Opus 5 also delegates to subagents more readily than prior models; see Controlling subagent spawning for guidance and a sample damping prompt.

If you're seeing excessive subagent use, add explicit guidance about when subagents are and aren't warranted:

Use subagents when tasks can run in parallel, require isolated context, or involve
independent workstreams that don't need to share state. For simple tasks, sequential
operations, single-file edits, or tasks where you need to maintain context across steps,
work directly rather than delegating.

Chain complex prompts

With adaptive thinking and subagent orchestration, Claude handles most multistep reasoning internally. Explicit prompt chaining (breaking a task into sequential API calls) is still useful when you need to inspect intermediate outputs or enforce a specific pipeline structure.

The most common chaining pattern is self-correction: generate a draft → have Claude review it against criteria → have Claude refine based on the review. Each step is a separate API call so you can log, evaluate, or branch at any point.

Reduce file creation in agentic coding

Claude's latest models may sometimes create new files for testing and iteration purposes, particularly when working with code. This approach allows Claude to use files, especially Python scripts, as a 'temporary scratchpad' before saving its final output. Using temporary files can improve outcomes particularly for agentic coding use cases.

If you'd prefer to minimize net new file creation, you can instruct Claude to clean up after itself:

If you create any temporary new files, scripts, or helper files for iteration, clean up
these files by removing them at the end of the task.

Overeagerness

Claude Opus 4.5 and Claude Opus 4.6 have a tendency to overengineer by creating extra files, adding unnecessary abstractions, or building in flexibility that wasn't requested. If you're seeing this undesired behavior, add specific guidance to keep solutions minimal.

For example:

Avoid over-engineering. Only make changes that are directly requested or clearly
necessary. Keep solutions simple and focused:

- Scope: Don't add features, refactor code, or make "improvements" beyond what was
asked. A bug fix doesn't need surrounding code cleaned up. A simple feature doesn't need
extra configurability.

- Documentation: Don't add docstrings, comments, or type annotations to code you didn't
change. Only add comments where the logic isn't self-evident.

- Defensive coding: Don't add error handling, fallbacks, or validation for scenarios
that can't happen. Trust internal code and framework guarantees. Only validate at system
boundaries (user input, external APIs).

- Abstractions: Don't create helpers, utilities, or abstractions for one-time
operations. Don't design for hypothetical future requirements. The right amount of
complexity is the minimum needed for the current task.

Avoid focusing on passing tests and hardcoding

Claude can sometimes focus too heavily on making tests pass at the expense of more general solutions, or may use workarounds like helper scripts for complex refactoring instead of using standard tools directly. To prevent this behavior and get solutions that generalize:

Please write a high-quality, general-purpose solution using the standard tools
available. Do not create helper scripts or workarounds to accomplish the task more
efficiently. Implement a solution that works correctly for all valid inputs, not just
the test cases. Do not hard-code values or create solutions that only work for specific
test inputs. Instead, implement the actual logic that solves the problem generally.

Focus on understanding the problem requirements and implementing the correct algorithm.
Tests are there to verify correctness, not to define the solution. Provide a principled
implementation that follows best practices and software design principles.

If the task is unreasonable or infeasible, or if any of the tests are incorrect, please
inform me rather than working around them. The solution should be robust, maintainable,
and extendable.

Minimizing hallucinations in agentic coding

Claude's latest models are less prone to hallucinations and give more accurate, grounded, intelligent answers based on the code. To encourage this behavior even more and minimize hallucinations:

<investigate_before_answering>
Never speculate about code you have not opened. If the user references a specific file,
you MUST read the file before answering. Make sure to investigate and read relevant
files BEFORE answering questions about the codebase. Never make any claims about code
before investigating unless you are certain of the correct answer - give grounded and
hallucination-free answers.
</investigate_before_answering>