# Work Remaining
This section describes what remains to be done to bring the current CleverAgents codebase up to the specification.
**Last Updated:** February 6, 2026
### Current State Assessment
#### What's Implemented
Based on analysis of the current codebase:
1. **Plan Lifecycle Foundation** - The 4-phase lifecycle (Action → Strategize → Execute → Apply), where Action serves as a reusable template stage, is partially implemented in `plan_lifecycle_service.py`
2. **Database Models** - Models exist for projects, plans, contexts, changes, and actors
3. **LangGraph Integration** - Graph-based workflow support exists but is not fully connected to the plan workflow
4. **Reactive System** - A reactive system with stream routing is present
5. **Actor System** - Actor models and services exist but lack full behavioral definitions
6. **Basic Context Analysis** - Simple context loading and analysis capabilities
7. **Change Tracking** - Basic `Change` and `ChangeSet` models exist
#### What's Missing
Critical gaps between the specification and current implementation:
1. **No Resource Abstraction** - The unified resource layer for files, databases, APIs is entirely missing
2. **No Sandboxing** - Execute phase writes directly to files without isolation
3. **No Decision Tree** - No decision tracking, storage, or correction mechanism
4. **No Skills/MCP Integration** - No skill registry, skill YAML parsing, tool adapters, or MCP protocol support
5. **Single-File Limitation** - Hard-coded to generate exactly one file per plan
6. **Text-Based Code Generation** - Still parsing LLM output instead of tool-based approach
7. **No Checkpointing** - No rollback or checkpoint capabilities
8. **No Code Intelligence / ACMS** - Missing ACMS implementation: UKO ontology analyzers, Context Assembly Pipeline (the 10-component pluggable pipeline for strategy orchestration, fragment fusion, and context finalization), CRP skill, context budget calculator, and backend indices (text, vector, graph). The full design is specified in the "Advanced Context Management System (ACMS)" section.
9. **No Context Tiers** - No hot/warm/cold memory architecture, no warm-to-cold demotion, no temporal versioning of UKO nodes
10. **Limited Validation** - Basic stub validation instead of semantic checks
### Work Items by Priority
### 1) Implement Tool-Based Resource Modification (Critical Foundation)
#### Problem
The current system generates code as a single text blob that gets written to one file. The specification requires a modern tool-based approach where LLMs invoke discrete operations on resources.
#### Implementation Steps
1. **Create Resource Abstraction Layer**
# New modules needed:
src/cleveragents/domain/models/resources.py
src/cleveragents/domain/resources/handlers.py
src/cleveragents/domain/resources/sandbox.py
# Core file operations (provided by built-in skill groups)
read_file(path: str) -> str
write_file(path: str, content: str) -> None
edit_file(path: str, changes: list[Edit]) -> None
delete_file(path: str) -> None
move_file(src: str, dst: str) -> None
create_directory(path: str) -> None
list_files(pattern: str) -> list[str]
search_files(pattern: str, content_pattern: str) -> list[Match]
class Sandbox(Protocol):
def create() -> SandboxRef
def read(path: str) -> Content
def write(path: str, content: Content) -> Change
def diff() -> DiffView
def commit() -> None
def rollback() -> None
-- New tables needed
CREATE TABLE decisions (
decision_id TEXT PRIMARY KEY, -- ULID
plan_id TEXT NOT NULL,
parent_decision_id TEXT,
decision_type TEXT NOT NULL,
question TEXT NOT NULL,
chosen_option TEXT NOT NULL,
alternatives_considered TEXT, -- JSON array
context_snapshot TEXT NOT NULL, -- JSON
created_at TEXT NOT NULL
);
agents plan correct <decision_id> --mode=revert --guidance "<new decision>"
actors:
my_strategist:
type: graph
config:
provider: anthropic
model: claude-3-opus
memory_policy: per_plan
context_view: architect # High-level view
skills:
- local/file-ops # provides read_file
- local/code-intelligence # provides search_code, analyze_dependencies
routes:
strategize:
entry_point: analyze
nodes:
- name: analyze
type: llm
- name: plan
type: llm
class MCPToolAdapter:
def wrap_mcp_tool(self, tool) -> Tool:
# Add sandbox interception
# Add change tracking
# Add capability metadata
# Add resource binding support
class ToolCapability:
read_only: bool
write_scope: list[str] # References resource slot names
checkpointable: bool
idempotent: bool
side_effects: list[str]
# In tool or skill YAML configuration
mcp_servers:
- name: github
command: "npx @anthropic/mcp-github"
env: {GITHUB_TOKEN: "${GITHUB_TOKEN}"}
# Actor references a skill containing validation tools
skills:
- local/semantic-validators # validate_api_compatibility,
# check_invariants, verify_test_coverage
# Register validations (tool subtypes)
agents validation add --config ./validations/run-tests.yaml --required local/run-tests
agents validation add --config ./validations/lint-check.yaml --required local/lint-check
# Attach to resource (directly or scoped to a project)
agents validation attach --project my-api my-api-repo local/run-tests
agents validation attach my-api-repo local/lint-check
class PlanLifecycleGraph:
def strategize_subgraph(self) -> StateGraph
def execute_subgraph(self) -> StateGraph
def apply_subgraph(self) -> StateGraph