Summary
Add a planning layer and multi-step agent capability so agents can decompose tasks into ordered subtasks, maintain context across steps, and reliably execute multi-action workflows.

Motivation
Current single-turn agents are limited for complex tasks that require planning, intermediate decisions, or external actions (APIs, I/O). Enabling multi-step agents will:

• Allow complex workflows (e.g., research → draft → verify → publish).
• Improve reliability by making execution explicit and recoverable.
• Make automation and orchestration use-cases practical.

Goals

• Provide a planner that decomposes high-level goals into discrete steps/actions.
• Provide a step executor that runs actions sequentially, maintains context, and handles failures/retries.
• Expose a clear API for building multi-step agents and plugging in tool actions (APIs, shell commands, database ops).
• Persist context and execution history so agents can resume and audit runs.

Non-goals

• Building domain-specific planners (we provide general primitives).
• Replacing orchestration systems (this complements them for agent-level planning).

Proposed design (high-level)

• Planner component

  • Input: goal + optional constraints
  • Output: ordered list of steps (each step: description, required tools, expected output)
  • Options: depth/iterations limits, cost/latency tradeoffs

• Executor component

  • Runs steps in order, invoking tool adapters
  • Maintains and updates context/memory between steps
  • Handles step failures (retry/backoff/skip/rollback) based on policy
  • Emits structured logs and events

• Tool Adapter layer

  • Standard interface for external integrations (HTTP, DB, file, shell, LLM calls)
  • Includes sandboxing/timeouts/rate-limit handling

• Persistence & Resume

  • Store plan, step states, intermediate outputs, and metadata
  • Support resuming from the last successful step

• Observability & Debugging

  • Structured execution logs, step-level metrics, and traceability
  • Optionally export to existing telemetry backends

Example flow

1. User: “Create a short blog post about X, include links and references.”
2. Planner: [Research → Outline → Draft → Fact-check → Finalize]
3. Executor:
   • Step 1: Research — call search API, store references.
   • Step 2: Outline — call LLM with references.
   • Step 3: Draft — generate content from outline.
   • Step 4: Fact-check — validate claims via APIs.
   • Step 5: Finalize — produce final post and metadata.

API / UX suggestions (pseudo)

• plan = planner.create_plan(goal="Write blog post about X", constraints={length:300-500})
• run_id = executor.execute(plan, resume=false, on_step=callback)
• executor.resume(run_id)

Acceptance criteria (testable)

• An agent can accept a high-level goal and produce a multi-step plan.
• Steps are executed in order and intermediate outputs are accessible.
• Failures are detected and a configurable retry strategy is applied.
• Execution can be paused and resumed with preserved context.
• Tool adapters can be added with a documented interface.
• Logs show step-level inputs/outputs for auditing.

Implementation notes / priorities

• Phase 1: Minimal planner + sequential executor + in-memory persistence + 2-3 core tool adapters (HTTP, file, LLM).
• Phase 2: Durable storage, advanced retry/rollback policies, UI for monitoring, plugin system.
• Keep planner as a replaceable strategy so teams can swap simple heuristics for advanced planners..