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Framework architecture

Documentation index · Workflow authoring

FrontierAgent separates the reusable agent runtime from workflow plugins, tool plugins, and benchmark evaluation. The framework layer has no dependency on benchmarks; CI enforces that boundary with a framework-only import smoke.

frontier_agent/   generic loop, scheduling, registries, AgentBus, observers
plugins/tools/   tool implementations and sandbox policy
workflows/       pipeline specs, profiles, prompts, workflow-owned observers
benchmarks/      public harness plus bundled FrontierSearchBench/FrontierChallenge

Runtime flow

PipelineSpec describes nodes, state visibility, transitions, and terminal nodes. Workflow plugins register specs and AgentDefinition objects. The scheduler builds the selected graph, resolves each node function, applies its ContextPolicy, and merges declared outputs back into pipeline state.

Agent nodes call run_agent_loop, the domain-neutral ReAct kernel:

result = await run_agent_loop(
    system_prompt=system_prompt,
    user_message=question,
    llm=llm,
    tools=tools,
    config=loop_config,
    observers=observers,
    model_profile=model_profile,
)

The loop binds tools and session identity, calls the LLM, parses tool calls, executes authorized tools, notifies observers, and applies compaction. Its implementation is split into focused modules under frontier_agent/core/runtime/loop/: _bind, _call, _streaming, _response, _runaway, and _tool. llm_client remains the stable compatibility facade for existing imports. Workflow semantics such as planning, terminal-tool behavior, reporter routing, and recovery belong outside the kernel.

Observer contract

Observers can implement only the callbacks they need:

Callback Purpose
on_loop_start Initialize per-run state
on_llm_attempt Observe retries, provider attempts, and failures
on_llm_delta Inspect streaming chunks
on_llm_response Validate or alter a completed assistant turn
on_tool_call Approve, annotate, or interrupt a tool call
on_tool_result Inspect and reshape tool outcomes
on_turn_end Apply turn-level stopping or retry policy
on_loop_end Final telemetry and cleanup
on_loop_cancelled Cancellation-safe cleanup

Callbacks return an Intervention when they need to stop, retry, replace content, or continue without consuming the normal turn budget. Observer failures are handled according to the loop contract; cleanup and telemetry should remain best-effort, while authorization observers must fail closed.

Agent teams

AgentBus provides task submission, messaging, report collection, cancellation, and shared context. SpawnGuard limits nesting, parallelism, and task budgets. The tool registry exposes only the explicit OSS allowlist; adding a Python module under plugins/tools/ does not automatically make it agent-accessible.

Sandboxing

File and shell tools share a task-scoped sandbox. Supported backend values are:

  • auto: probe bubblewrap and fail with guidance if isolation is unavailable.
  • bwrap: require bubblewrap and Linux user namespaces.
  • container: trust the surrounding task container as the isolation boundary.

There is no unisolated host fallback. /inputs is read-only, /workspace is the working directory, and /outputs is the only persistent deliverable location for file benchmarks. Network and path policies are applied before command execution, and authorization or sandbox failures are fail-closed.

Output publication is manifest-aware: only declared publishers may write final deliverables, and /outputs/scratch is reserved for persisted intermediate work. Agent Team sub-agents receive scoped workspaces while sharing approved inputs and outputs.

The interactive terminal adds a second layer on top of this — an approval gate, hard denials that survive --yes, and a journal backing /revert. Those are documented in apodex/README.md, and the trace and log paths each session writes in run artifacts and timestamps.

Package boundaries

Base dependencies run the framework and workflows. Install plugins for optional third-party tool SDKs, sandbox and document-readers for file tools, and eval for benchmark datasets, Harbor, and judges. This keeps framework consumers from installing the evaluation stack.