Starter Agents

Akshi ships with four agent templates that demonstrate common patterns. Use them as starting points for your own agents.

Create any template with:

akshi create agent <name> --template <template>

log-monitor

akshi create agent my-log-monitor --template log-monitor

Watches *.log files in its workspace directory for new entries. When new lines appear, the agent sends them to the inference broker for severity classification (info, warning, error, critical). Classified findings are posted back to the runtime log stream with structured annotations.

Use case: Automated log triage. Point it at application or system logs and let the LLM surface the entries that matter.

Key patterns demonstrated:

• File-watching via workspace directory polling
• Batched inference calls with structured output parsing
• Severity-based filtering logic

code-reviewer

akshi create agent my-code-reviewer --template code-reviewer

Watches for *.diff files in its workspace. When a new diff appears, the agent reads it, sends the patch to the inference broker with a code-review prompt, and writes review comments back as a structured output file.

Use case: Lightweight automated code review. Drop a git diff output into the workspace and get feedback without leaving the terminal.

Key patterns demonstrated:

• File-triggered agent execution
• Multi-turn prompt construction (system prompt + diff context)
• Structured output (review comments with line references)

test-runner

akshi create agent my-test-runner --template test-runner

Watches for test output files (e.g., JUnit XML, TAP, or plain-text test logs). When a new result file appears, the agent parses it and classifies each failure as a likely flake, environment issue, or genuine regression using the inference broker. Results are written to the agent database.

Use case: CI failure triage. Feed test output into the workspace and get a categorized breakdown of what failed and why.

Key patterns demonstrated:

• Parsing structured and semi-structured test output
• Classification with confidence scores
• Database writes via db_exec

research-lead

akshi create agent my-research-lead --template research-lead

Takes query files (plain text with a research question) from its workspace. For each query, the agent:

1. Uses the MCP search tool to find relevant sources.
2. Fetches and scrapes the top URLs.
3. Synthesizes findings into a journal-style dossier written back to the workspace.

Use case: Automated research briefs. Drop a question file in and receive a sourced summary document.

Key patterns demonstrated:

• MCP tool invocation (search, fetch)
• Multi-step agent workflow (search, retrieve, synthesize)
• Long-form structured output generation

Customizing templates

Each template is a standard Rust project. After scaffolding, you own the code and can modify it freely:

• Change the file patterns the agent watches.
• Adjust the prompts sent to the inference broker.
• Add additional SDK calls (database, logging, MCP tools).
• Tune fuel_limit and workspace permissions in runtime.toml.

See the First Agent guide for details on the build and configuration workflow.