### 1. Treat the harness as first-class product IP

The orchestrator is the product. Invest in:

- context selection
- task planning
- tool descriptions
- retries and recovery
- permission policies
- durable state and memory
- evaluation loops

### 2. Long-running agents need explicit state management

If an agent will span many turns or run in the background, it cannot rely on raw transcript accumulation. It needs:

- compact task state
- durable artifacts and handoff files
- summarized history
- selective retrieval of only relevant prior work

### 3. Safety needs multiple layers

The practical stack is not one feature. It is a combination of:

- conservative defaults
- scoped permissions
- sandboxing where possible
- action classification
- audit logs
- destructive-action testing
- prompt-injection defenses

### 4. Local agents create real endpoint risk

A coding agent with shell and filesystem access is effectively privileged software. That means release hygiene matters:

- do not ship source maps in production artifacts
- scan release bundles before publish
- use artifact signing / attestation
- minimize local plaintext retention where possible
- document what is logged, where, and why

## How to Be Effective with Context Engineering

Anthropic defines context engineering as curating and maintaining the right set of tokens and state around a model invocation, not just writing a better prompt. For an agentic CLI, the practical meaning is simpler: the system should always provide the model with enough context to take the next correct action, but not so much that it becomes distracted, expensive, or unsafe.

### A more useful working definition

For a coding agent, context is not just the system prompt. It is the full operating environment:

- the active task and constraints
- the current plan and stopping condition
- the relevant files, symbols, and diffs
- the available tools and their contracts
- the recent observations from shell commands and tests
- durable memory from earlier work
- the policy boundary around permissions and risky actions

If any of those are missing, stale, or too noisy, agent quality drops fast.

### The context stack a coding CLI should manage

Treat context as a layered stack, not a single blob:

1. **Stable policy layer**  
   The non-negotiables: system rules, tool permissions, repo conventions, sandbox limits, output style, and safety constraints.

2. **Task layer**  
   The user's request, the success condition, assumptions, and explicit non-goals. This should be short and durable.

3. **Working-state layer**  
   The current plan, what has already been tried, what remains blocked, and which files or services are in scope.

4. **Evidence layer**  
   The actual code snippets, command results, test failures, stack traces, and docs needed for the next decision.

5. **Memory layer**  
   Reusable facts worth carrying across turns, such as build quirks, repo-specific commands, and previous failed approaches.

Most agent failures happen when these layers are mixed together without discipline.

### Opinionated rules for agent and CLI design

#### 1. Keep the task state outside the transcript

Do not rely on the model to infer the current plan from chat history. Persist a compact state object or artifact containing:

- the objective
- current step
- files in scope
- known constraints
- open questions
- last meaningful result

The transcript is a bad database. Use it for conversation, not state recovery.

#### 2. Retrieve code narrowly and late

Do not dump entire files or directories into context by default. Retrieve only what the next step needs:

- a specific symbol
- a failing test
- a diff hunk
- a bounded file region
- a targeted doc excerpt

Broad retrieval creates distraction and raises token cost without improving decisions.

#### 3. Summarize after every expensive step

After a search pass, test run, or multi-command investigation, convert the result into a short structured summary before moving on. Good summaries should capture:

- what was learned
- what changed
- what remains uncertain
- what the next action should be

This keeps the working set fresh and prevents context drift across long sessions.

#### 4. Design tools to return decision-ready output

Tool output should help the model choose the next action, not force it to parse noise. Prefer:

- concise command output
- bounded file reads
- explicit exit codes
- normalized error messages
- machine-parseable fields where possible

If a tool returns pages of raw text, the tool is poorly designed for agent use.

#### 5. Make memory write-worthy, not chatty

Persistent memory should be rare and high-value. Store only facts that are likely to matter later, such as:

- the right test command for this repo
- a non-obvious setup requirement
- a dangerous directory or workflow to avoid
- a service dependency that causes common failures

Do not store transient observations that belong in the current task state only.

#### 6. Separate planning context from execution context

The model needs different context when deciding what to do than when editing a file or running a command. A good CLI can tighten the context window for execution:

- include only the target file and local constraints for edits
- include only the exact command intent and safety policy for shell execution
- include only the relevant failure output for debugging

This reduces accidental spillover from stale earlier reasoning.

#### 7. Build explicit stop conditions

Agents burn time when they do not know when to stop. Every substantial task should carry one of these end states:

- requested change implemented
- tests passing or best-available verification complete
- blocked on missing permission or missing information
- unsafe to continue without user confirmation

Without a stop condition, context engineering degrades into aimless looping.

### Common failure modes to design against

These are the recurring context failures in coding agents:

- **Context poisoning:** irrelevant logs, stale plans, or old diffs dominate the prompt.
- **Context starvation:** the model is asked to act without the relevant file region, command result, or policy detail.
- **Context collision:** instructions from different phases conflict, such as planning guidance leaking into final output formatting.
- **Context amnesia:** the agent forgets prior discoveries because nothing durable was written down.
- **Context bloat:** every turn carries too much history, so quality drops and latency rises.

Your CLI should have explicit mechanisms to detect and correct each of these.

### A tactical operating loop

For a coding agent, a strong default loop looks like this:

1. Restate the goal and define success.
2. Gather only the minimum code and repo context needed to choose the next step.
3. Write or update compact task state.
4. Execute one meaningful action.
5. Summarize the result into durable working state.
6. Prune stale context before the next step.
7. Stop as soon as the success condition or block condition is reached.

This is the operational core behind most reliable agent behavior.

### What the Claude Code leak suggests here

The leak matters because it reinforces that strong coding agents are mostly a context-management problem wrapped around a model:

- permission logic is context engineering
- tool orchestration is context engineering
- background execution is context engineering
- memory and handoff artifacts are context engineering
- safety boundaries are context engineering

That is the practical takeaway: do not hunt for a magic prompt. Build a system that keeps the right context available at the right time.

## Practical Takeaways

If the goal is to design a strong agentic CLI, the combined lesson is:

- Do not over-focus on prompt wording.
- Invest in context assembly, memory, tool quality, and evaluations.
- Keep the architecture simple until complexity is justified.
- Treat local execution and packaging as security-sensitive.
- Treat context as core infrastructure, not support work.

## Sources

- [Effective context engineering for AI agents | Anthropic](https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents)
- [Building Effective AI Agents | Anthropic](https://www.anthropic.com/research/building-effective-agents)
- [Writing effective tools for AI agents | Anthropic](https://www.anthropic.com/engineering/writing-tools-for-agents)
- [Best practices for prompt engineering with the OpenAI API | OpenAI Help Center](https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-the-openai-api)
