Summary
Before the autonomy improvements in #27, HelixAgent demonstrated an agent-style workflow, but it was not a fully autonomous agent runtime.
The original implementation could accept a prompt, build a small fixed execution graph, call predefined tools, and return a response. That showed orchestration, but it did not provide the persistent control loop or operational safeguards required for an agent to pursue goals independently over multiple steps.
Why the previous implementation was not fully autonomous
Fixed workflow instead of an adaptive control loop
Execution followed a predetermined planning/tool sequence. The runtime did not continuously:
- assess the current state,
- select the next task,
- execute and observe results,
- revise the plan when evidence changed, and
- continue until a terminal condition was reached.
Without that plan → execute → observe → replan loop, the system behaved more like a scripted workflow than an autonomous agent.
No durable run state
Agent state existed only during the active request. There was no persisted run record containing:
- the goal and current plan,
- completed and pending tasks,
- tool observations,
- retry history,
- approval decisions,
- execution status, or
- final outcome.
A process restart or request interruption therefore prevented reliable recovery and continuation.
No explicit resource budgets
The runtime lacked enforceable limits for:
- planning iterations,
- tool calls,
- retries, and
- execution time.
A safe autonomous system needs bounded execution so that faulty plans or tools cannot loop indefinitely or consume resources without control.
No governed tool boundary
Tools were callable without a unified registry describing their purpose, risk level, timeout, and execution policy. Failures were not consistently normalized, and slow tools did not have a reliable non-blocking timeout boundary.
No human approval gates
The system did not distinguish read-only actions from actions that could modify data or external systems. Consequently, it had no mechanism to pause a run, request approval for a risky action, persist that decision, and resume safely.
No resumable run API
The API exposed prompt execution, but not a lifecycle for autonomous work. Clients could not submit a run, inspect its state later, respond to an approval request, or resume execution by run ID.
Planning was not cleanly extensible
Planning behavior was coupled to the existing implementation rather than defined through a typed planner contract. This made it harder to introduce structured-output model planners while preserving deterministic testing and runtime invariants.
Improvements introduced in #27
PR #27 establishes an autonomy foundation with:
- typed goals, tasks, observations, approvals, and run states;
- a budgeted plan/execute/observe/replan control loop;
- SQLite-backed checkpoints and resumable run IDs;
- retry and terminal-state handling;
- a governed tool registry with risk metadata and timeouts;
- explicit approval gates for write-risk tools;
- asynchronous run submission, status lookup, and approval endpoints;
- a pluggable planner protocol; and
- behavioral and API tests for the new lifecycle.
Definition of done
This issue can be closed when #27 is merged and the repository has verified:
- durable run recovery after process restart;
- bounded iteration and tool execution;
- reliable timeout and retry behavior;
- persisted approval pause/approve/deny flows;
- status inspection and resumption by run ID; and
- documentation that distinguishes the deterministic default planner from future model-backed planning.
Follow-up work
The autonomy foundation does not by itself make the service a complete production autonomous platform. Subsequent improvements should add:
- a provider-specific structured-output model planner and critic;
- an external durable worker queue;
- authentication, authorization, and per-tool policy controls;
- distributed run leases and idempotency protections;
- richer tracing, evaluation, and audit events; and
- sandboxing for tools that execute code or mutate external systems.
Related implementation: #27
Summary
Before the autonomy improvements in #27, HelixAgent demonstrated an agent-style workflow, but it was not a fully autonomous agent runtime.
The original implementation could accept a prompt, build a small fixed execution graph, call predefined tools, and return a response. That showed orchestration, but it did not provide the persistent control loop or operational safeguards required for an agent to pursue goals independently over multiple steps.
Why the previous implementation was not fully autonomous
Fixed workflow instead of an adaptive control loop
Execution followed a predetermined planning/tool sequence. The runtime did not continuously:
Without that plan → execute → observe → replan loop, the system behaved more like a scripted workflow than an autonomous agent.
No durable run state
Agent state existed only during the active request. There was no persisted run record containing:
A process restart or request interruption therefore prevented reliable recovery and continuation.
No explicit resource budgets
The runtime lacked enforceable limits for:
A safe autonomous system needs bounded execution so that faulty plans or tools cannot loop indefinitely or consume resources without control.
No governed tool boundary
Tools were callable without a unified registry describing their purpose, risk level, timeout, and execution policy. Failures were not consistently normalized, and slow tools did not have a reliable non-blocking timeout boundary.
No human approval gates
The system did not distinguish read-only actions from actions that could modify data or external systems. Consequently, it had no mechanism to pause a run, request approval for a risky action, persist that decision, and resume safely.
No resumable run API
The API exposed prompt execution, but not a lifecycle for autonomous work. Clients could not submit a run, inspect its state later, respond to an approval request, or resume execution by run ID.
Planning was not cleanly extensible
Planning behavior was coupled to the existing implementation rather than defined through a typed planner contract. This made it harder to introduce structured-output model planners while preserving deterministic testing and runtime invariants.
Improvements introduced in #27
PR #27 establishes an autonomy foundation with:
Definition of done
This issue can be closed when #27 is merged and the repository has verified:
Follow-up work
The autonomy foundation does not by itself make the service a complete production autonomous platform. Subsequent improvements should add:
Related implementation: #27