# Harness Agent Samples

This folder demonstrates `create_harness_agent` — a factory function that builds a
pre-configured, batteries-included agent by assembling the full agent pipeline
from a chat client.

## What is `create_harness_agent`?

`create_harness_agent` bundles the following features into a single `Agent` instance:

| Feature | Description |
|---------|-------------|
| Function invocation | Automatic tool calling loop |
| Per-service-call persistence | History persisted after every model call |
| Compaction | Context-window management (sliding window + tool result compaction) |
| TodoProvider | Todo list management for planning and tracking |
| AgentModeProvider | Plan/execute mode tracking |
| MemoryContextProvider | File-based durable memory (when `memory_store` provided) |
| SkillsProvider | File-based skill discovery and progressive loading |
| Shell tool | Shell command execution + environment probing (when `shell_executor` provided) |
| Tool approval | "Don't ask again" standing rules + heuristic auto-approval (enabled by default) |
| Looping | Re-invoke the agent until a `loop_should_continue` predicate is satisfied (when provided) |
| OpenTelemetry | Built-in observability |

Each feature can be disabled or customized via keyword arguments.

## Samples

| File | Description |
|------|-------------|
| `harness_research.py` | Interactive research assistant with web search, a plan/execute workflow, and an execute-mode loop that re-invokes the agent until every todo is complete |
| `harness_data_processing.py` | Data-processing assistant over a folder of CSV files, demonstrating file-access tools and tool approval |
| [`build_your_own_claw/`](./build_your_own_claw/README.md) | *Build your own claw* blog series — a personal finance assistant built step by step |

## Running

```bash
# Set your Foundry environment variables
export FOUNDRY_PROJECT_ENDPOINT="https://your-project.services.ai.azure.com/api/projects/your-project-name"
export FOUNDRY_MODEL="your-model-deployment-name"

# Authenticate with Azure (required for AzureCliCredential)
az login

# Run a sample against the released agent-framework (PEP 723 isolated env)
uv run samples/02-agents/harness/harness_research.py
```

### Running against the local repo

To run a sample against your **local** `agent-framework` checkout (so it picks
up uncommitted changes), use the workspace environment instead of the isolated
PEP 723 env. From the `python/` directory, run the script with `uv run python`
and add the `textual` UI dependency the harness console needs:

```bash
uv run --with textual python samples/02-agents/harness/harness_research.py
uv run --with textual python samples/02-agents/harness/harness_data_processing.py
```

The workspace environment already provides the editable `agent-framework`
packages plus the samples' other dependencies (`rich`, `python-dotenv`,
`azure-identity`); only `textual` needs to be supplied with `--with`.

> Note: invoking `uv run python <script>` (with `python`) bypasses the PEP 723
> metadata and uses the workspace env; `uv run <script>` (without `python`)
> uses the isolated env with the released package.

## Key Concepts

### Minimal Setup

`create_harness_agent` requires only a chat client:

```python
from agent_framework import create_harness_agent
from agent_framework.foundry import FoundryChatClient
from azure.identity import AzureCliCredential

agent = create_harness_agent(
    client=FoundryChatClient(credential=AzureCliCredential()),
)
```

### With Compaction

Provide token budget parameters to enable automatic context-window compaction:

```python
agent = create_harness_agent(
    client=FoundryChatClient(credential=AzureCliCredential()),
    max_context_window_tokens=128_000,
    max_output_tokens=16_384,
)
```

### Further Customization

Disable or customize any feature:

```python
agent = create_harness_agent(
    client=client,
    max_context_window_tokens=128_000,
    max_output_tokens=16_384,
    name="my-agent",
    agent_instructions="Custom instructions here.",
    disable_todo=True,          # Skip todo management
    disable_mode=True,          # Skip plan/execute modes
    disable_compaction=True,    # Skip compaction
)
```

### Plan/Execute Workflow

The `AgentModeProvider` enables a two-phase workflow:
1. **Plan mode** — Interactive: the agent asks questions, creates todos, gets approval
2. **Execute mode** — Autonomous: the agent works through todos independently

### Shell Tool

Pass a shell executor (e.g. `LocalShellTool` from `agent-framework-tools`) to enable shell
command execution plus automatic environment probing via a `ShellEnvironmentProvider`. The
tool is only wired when the chat client supports shell tools; otherwise a warning is logged
and the shell tool/provider are skipped. The caller owns the executor's lifecycle.

```python
from agent_framework_tools.shell import LocalShellTool, ShellEnvironmentProviderOptions

async with LocalShellTool(acknowledge_unsafe=True) as shell:
    agent = create_harness_agent(
        client=client,
        max_context_window_tokens=128_000,
        max_output_tokens=16_384,
        shell_executor=shell,
        # Optional: customize environment probing.
        shell_environment_provider_options=ShellEnvironmentProviderOptions(probe_tools=("git", "python")),
    )
```


## Security Considerations

Several harness capabilities extend the agent's trust boundary to external systems the developer
configures. Each is opt-in and requires explicit configuration by the developer, who is responsible
for vetting the external service, agent, skill source, or provider before enabling it:

- **`background_agents`** (`BackgroundAgentsProvider`) — delegates work to developer-supplied agents,
  which receive input from the parent and whose output is fed back into its context. A compromised
  agent could exfiltrate data or inject adversarial content via indirect prompt injection. Vet all
  supplied agents.
- **External skill sources** (`skills_provider` with e.g. `MCPSkillsSource`) — load skill content,
  and potentially scripts, from a remote source. A compromised source could return adversarial skills
  (indirect prompt injection) or exfiltrate data. Only enable sources you trust.
- **`AgentLoopMiddleware.with_judge`** — sends the request and the agent's latest response to a second,
  external judge chat client on every iteration. A compromised judge could exfiltrate that data or
  return manipulated feedback. Trust the judge as much as the primary model.
- **`SummarizationStrategy`** (via `before_compaction_strategy` / `after_compaction_strategy`) — calls
  out to an LLM whose output permanently becomes chat history. A compromised summarization service
  could inject unsafe, persistent instructions. Only use a service you trust as much as the primary
  model.
- **Telemetry** — when observability is enabled, telemetry destinations are developer-configured.
  Default telemetry is metadata only; enabling sensitive data additionally emits raw message content,
  tool arguments, and tool results. See the [observability samples](../observability/README.md).