Production-Grade Agentic System Implementation Plan

Executive Summary

This document provides a comprehensive work breakdown structure (WBS) for implementing all Seven Layers of Production-Grade Agentic Systems in Engram. Based on the maturity assessment (current: ⭐⭐⭐☆☆ 3.0/5.0), this plan prioritizes critical gaps and provides actionable tasks with dependencies.

Target Maturity: ⭐⭐⭐⭐⭐ (5.0/5.0)

Current State Summary

Layer Current Rating Target Rating Priority Estimated Effort
Layer 1: Interaction ⭐⭐⭐☆☆ (3.0) ⭐⭐⭐⭐⭐ (5.0) High 4 weeks
Layer 2: Orchestration ⭐⭐⭐⭐☆ (3.75) ⭐⭐⭐⭐⭐ (5.0) Medium 3 weeks
Layer 3: Cognition ⭐⭐⭐☆☆ (2.67) ⭐⭐⭐⭐⭐ (5.0) High 4 weeks
Layer 4: Memory ⭐⭐⭐☆☆ (3.33) ⭐⭐⭐⭐⭐ (5.0) Medium 3 weeks
Layer 5: Tools ⭐⭐⭐☆☆ (3.0) ⭐⭐⭐⭐⭐ (5.0) Medium 3 weeks
Layer 6: Guardrails ⭐☆☆☆☆ (0.8) ⭐⭐⭐⭐⭐ (5.0) CRITICAL 4 weeks
Layer 7: Observability ⭐⭐⭐☆☆ (3.0) ⭐⭐⭐⭐⭐ (5.0) High 3 weeks

Total Estimated Effort: 24 weeks (6 months)

Agent Integration with GitHub Projects

Elena and Marcus are authorized to interact with GitHub Projects to track implementation progress. Both agents have access to:

  • ✅ Create GitHub issues for tasks
  • ✅ Update issue status and progress
  • ✅ Query project status and metrics
  • ✅ List assigned tasks
  • ✅ Close completed tasks

Authorization: Agents use a GitHub Personal Access Token (configured via GITHUB_TOKEN environment variable) with repo, read:project, and write:project scopes.

System Awareness: The Engram system is aware of GitHub Projects progress through:

  • Agent queries to get_project_status tool
  • Automatic issue creation for new tasks
  • Progress tracking via issue state (open/closed)
  • Status reports generated from GitHub data

See docs/GitHub-Integration-Authorization.md for detailed setup and authorization model.

Phase 1: Critical Security & Safety (Weeks 1-4)

🚨 Layer 6: Guardrails - CRITICAL PRIORITY

Current State: ⭐☆☆☆☆ (0.8/5.0) - MUST FIX BEFORE PRODUCTION

Task 6.1: Input Guardrails Implementation

Priority: Critical | Effort: 1.5 weeks | Dependencies: None

Sub-tasks:

  • 6.1.1 Create backend/guardrails/input_guard.py module
    • Implement prompt injection detection using Rebuff or Microsoft Presidio
    • Add jailbreak pattern detection (DAN mode, role-playing, etc.)
    • Create detect_prompt_injection(text: str) -> bool function
    • Add logging for all filtered inputs (audit trail)
  • 6.1.2 PII Redaction Middleware
    • Integrate Microsoft Presidio or similar PII detection library
    • Create redact_pii(text: str) -> str function
    • Support: SSN, credit cards, email addresses, IP addresses, phone numbers
    • Add configuration for custom PII patterns (internal IDs, etc.)
  • 6.1.3 Input Validation Pipeline
    • Create InputGuardrails class with validate_input(text: str) -> GuardResult
    • Integrate into FastAPI middleware (backend/api/middleware/guardrails.py)
    • Apply to all /api/v1/chat/* and /api/v1/agents/* endpoints
    • Return structured errors (don’t expose detection patterns)

Acceptance Criteria:

  • All user inputs pass through guardrails before reaching LLM
  • Prompt injection attempts are detected and logged
  • PII is automatically redacted before LLM calls
  • Audit log contains all filtered inputs with timestamps

Files to Create/Modify:

  • backend/guardrails/__init__.py (new)
  • backend/guardrails/input_guard.py (new)
  • backend/api/middleware/guardrails.py (new)
  • backend/api/routers/chat.py (modify - add middleware)
  • backend/api/routers/agents.py (modify - add middleware)

Task 6.2: Execution Guardrails

Priority: High | Effort: 1 week | Dependencies: 6.1

Sub-tasks:

  • 6.2.1 Rate Limiting Implementation
    • Add Redis-based rate limiting (backend/guardrails/rate_limiter.py)
    • Per-user limits: 100 requests/hour, 10 requests/minute
    • Per-tenant limits: 1000 requests/hour
    • Per-session limits: 50 requests/hour
    • Return 429 with retry-after header
  • 6.2.2 Policy Engine (Open Policy Agent)
    • Deploy OPA server or use OPA-as-a-Service
    • Create policy files for tool call restrictions:
      • policies/tool_call_policy.rego - No delete operations
      • policies/external_access_policy.rego - No external emails/APIs
      • policies/data_access_policy.rego - Tenant-scoped data access
    • Integrate OPA client into backend/guardrails/policy_engine.py
    • Validate tool calls before execution
  • 6.2.3 Cost Limits
    • Track token usage per session in workflow state
    • Implement cost calculation (tokens × model cost)
    • Add session cost limits: $2.00 per session, $10.00 per user/day
    • Auto-terminate workflow if limit exceeded
    • Alert on cost threshold (80% of limit)

Acceptance Criteria:

  • Rate limits enforced at API level
  • Tool calls validated against OPA policies
  • Cost limits prevent “denial of wallet” scenarios
  • All violations logged with user/tenant context

Files to Create/Modify:

  • backend/guardrails/rate_limiter.py (new)
  • backend/guardrails/policy_engine.py (new)
  • policies/tool_call_policy.rego (new)
  • policies/external_access_policy.rego (new)
  • backend/workflows/agent_workflow.py (modify - add cost tracking)

Task 6.3: Output Guardrails

Priority: High | Effort: 1 week | Dependencies: 6.1

Sub-tasks:

  • 6.3.1 Hallucination Detection
    • Implement LLM-as-Judge pattern (backend/guardrails/hallucination_detector.py)
    • Use GPT-4o-mini or Claude Haiku as judge (cost-effective)
    • Compare agent output against retrieved context
    • Score: 0.0 (hallucination) to 1.0 (grounded)
    • Flag outputs with score < 0.7
  • 6.3.2 Topic/Tone Filtering
    • Create topic classifier for out-of-scope responses
    • Filter inappropriate language using content moderation API
    • Block competitor information if not authorized
    • Prevent financial/medical advice if not authorized
  • 6.3.3 Output Validation Pipeline
    • Create OutputGuardrails class
    • Integrate into agent response pipeline
    • Return sanitized output or request human review
    • Log all filtered outputs for audit

Acceptance Criteria:

  • Hallucinations detected with >90% accuracy
  • Out-of-scope topics filtered automatically
  • All filtered outputs logged and reviewed
  • Human escalation for high-risk outputs

Files to Create/Modify:

  • backend/guardrails/hallucination_detector.py (new)
  • backend/guardrails/output_guard.py (new)
  • backend/agents/base.py (modify - add output validation)

Task 6.4: Circuit Breaker Pattern

Priority: Medium | Effort: 0.5 weeks | Dependencies: 6.2, 6.3

Sub-tasks:

  • 6.4.1 Circuit Breaker Implementation
    • Create backend/guardrails/circuit_breaker.py
    • Track consecutive failures per session
    • Trip after 3 consecutive failures
    • Track low confidence scores (< 0.5)
    • Trip on cost threshold exceeded
  • 6.4.2 Human Escalation
    • Create escalation workflow (backend/workflows/escalation_workflow.py)
    • Send notification to human operator
    • Store session state for review
    • Allow human to resume or terminate

Acceptance Criteria:

  • Circuit breaker trips on failure patterns
  • Human escalation triggered automatically
  • Session state preserved for review
  • Metrics tracked for circuit breaker events

Files to Create/Modify:

  • backend/guardrails/circuit_breaker.py (new)
  • backend/workflows/escalation_workflow.py (new)

Task 6.5: Compliance Mapping

Priority: Medium | Effort: 1 week | Dependencies: 6.1-6.4

Sub-tasks:

  • 6.5.1 NIST AI RMF Mapping
    • Map existing controls to NIST AI RMF categories
    • Document risk assessment for each agent capability
    • Create compliance dashboard (frontend/src/pages/Admin/Compliance.tsx)
    • Generate compliance reports
  • 6.5.2 ASL-3 Preparation (if needed)
    • Assess if ASL-3 is required for use case
    • Implement real-time classifiers if needed
    • Add offline monitors for CBRN threats

Acceptance Criteria:

  • All guardrails mapped to NIST AI RMF
  • Compliance dashboard shows current posture
  • Risk assessments documented for each layer

Files to Create/Modify:

  • docs/compliance/nist-ai-rmf-mapping.md (new)
  • frontend/src/pages/Admin/Compliance.tsx (new)

Phase 2: Production Reliability (Weeks 5-8)

Layer 3: Cognition - LLM Gateway & Reasoning

Task 3.1: LLM Gateway Implementation

Priority: High | Effort: 2 weeks | Dependencies: None

Sub-tasks:

  • 3.1.1 Deploy LiteLLM Gateway
    • Set up LiteLLM server (backend/llm/gateway.py)
    • Configure provider fallback chains (Azure → Anthropic → Gemini)
    • Add load balancing across deployments
    • Implement health checks for each provider
  • 3.1.2 Smart Routing
    • Create query complexity analyzer (backend/llm/complexity_analyzer.py)
    • Route simple tasks to GPT-4o-mini/Claude Haiku
    • Route complex reasoning to GPT-4o/Claude Sonnet
    • Route code generation to specialized models
    • Add routing rules configuration
  • 3.1.3 Cost Optimization
    • Track costs per model/provider
    • Implement cost-based routing (prefer cheaper models when appropriate)
    • Add cost dashboards (frontend/src/pages/Admin/CostGovernance.tsx)

Acceptance Criteria:

  • All LLM calls go through gateway
  • Smart routing reduces costs by 40%+
  • Fallback works automatically on provider failures
  • Cost tracking accurate to $0.01

Files to Create/Modify:

  • backend/llm/gateway.py (new)
  • backend/llm/complexity_analyzer.py (new)
  • backend/agents/base.py (modify - use gateway)
  • frontend/src/pages/Admin/CostGovernance.tsx (new)

Task 3.2: Structured Output Enforcement

Priority: High | Effort: 1 week | Dependencies: 3.1

Sub-tasks:

  • 3.2.1 PydanticAI Integration
    • Install and configure PydanticAI
    • Define output schemas for all agent responses
    • Create backend/agents/schemas.py with response types
    • Integrate into agent base class
  • 3.2.2 Self-Correction on Validation Failure
    • Catch validation errors
    • Feed errors back to model with correction prompt
    • Retry up to 3 times
    • Log validation failures for analysis

Acceptance Criteria:

  • All agent outputs validated against schemas
  • Validation failures trigger self-correction
  • <5% of responses require manual correction
  • Type-safe responses throughout system

Files to Create/Modify:

  • backend/agents/schemas.py (new)
  • backend/agents/base.py (modify - add PydanticAI)

Task 3.3: Advanced Reasoning Patterns

Priority: Medium | Effort: 1 week | Dependencies: 3.2

Sub-tasks:

  • 3.3.1 ReAct Loop Implementation
    • Add explicit ReAct pattern to agent reasoning
    • Capture thought/action/observation steps
    • Store reasoning trace for debugging
    • Add ReAct visualization in UI
  • 3.3.2 Chain-of-Thought Enforcement
    • Add CoT prompting for complex tasks
    • Capture reasoning steps in response
    • Display reasoning in UI (collapsible)

Acceptance Criteria:

  • ReAct loop visible in agent traces
  • Reasoning steps captured and displayable
  • Improved accuracy on multi-step tasks

Files to Create/Modify:

  • backend/agents/reasoning.py (new)
  • frontend/src/components/Agent/ReasoningTrace.tsx (new)

Layer 7: Observability - Evaluation & LLMOps

Task 7.1: LLMOps Platform Integration

Priority: High | Effort: 1.5 weeks | Dependencies: None

Sub-tasks:

  • 7.1.1 Arize Phoenix Deployment
    • Deploy Phoenix server or use cloud service
    • Integrate Phoenix SDK into agent workflows
    • Send execution traces to Phoenix
    • Configure trace visualization
  • 7.1.2 LangSmith Integration (optional)
    • Add LangSmith for debugging agent paths
    • Enable trace replay for failure analysis
    • Add LangSmith UI to admin panel

Acceptance Criteria:

  • All agent executions traced in Phoenix
  • Trace visualization shows full execution path
  • Can replay traces for debugging
  • Performance metrics visible in dashboard

Files to Create/Modify:

  • backend/observability/phoenix.py (new)
  • backend/workflows/agent_workflow.py (modify - add Phoenix tracing)

Task 7.2: Evaluation Framework

Priority: High | Effort: 1.5 weeks | Dependencies: 7.1

Sub-tasks:

  • 7.2.1 DeepEval Integration
    • Install DeepEval framework
    • Create golden datasets (tests/evals/golden_datasets/)
    • Define evaluation metrics:
      • Faithfulness (hallucination detection)
      • Answer Relevance
      • Context Precision
      • Response Completeness
    • Create eval test suite (tests/evals/test_agent_quality.py)
  • 7.2.2 Continuous Evaluation in CI/CD
    • Add eval step to GitHub Actions
    • Run evals on every PR
    • Block merge if evals fail
    • Track eval scores over time
  • 7.2.3 Online Evaluation (LLM-as-Judge)
    • Sample 10% of production interactions
    • Use GPT-4o as judge for quality scoring
    • Track quality drift over time
    • Alert on quality degradation

Acceptance Criteria:

  • Golden dataset with 50+ test cases
  • Eval pipeline runs in CI/CD
  • Quality metrics tracked in dashboard
  • Alerts on quality degradation

Files to Create/Modify:

  • tests/evals/golden_datasets/ (new directory)
  • tests/evals/test_agent_quality.py (new)
  • .github/workflows/evals.yml (new)
  • backend/observability/quality_monitor.py (new)

Task 7.3: Cost Governance

Priority: Medium | Effort: 1 week | Dependencies: 3.1

Sub-tasks:

  • 7.3.1 Per-Session Cost Tracking
    • Track costs in workflow state
    • Store costs in database
    • Create cost aggregation queries
  • 7.3.2 Cost Dashboards
    • Create cost dashboard (frontend/src/pages/Admin/CostGovernance.tsx)
    • Show costs per user/tenant/session
    • Add cost trends over time
    • Implement cost alerts
  • 7.3.3 Hard Budget Caps
    • Implement session cost limits (from Layer 6)
    • Add user/tenant daily limits
    • Auto-terminate on limit exceeded

Acceptance Criteria:

  • Cost tracking accurate to $0.01
  • Dashboards show real-time costs
  • Budget caps enforced automatically
  • Alerts sent on threshold breaches

Files to Create/Modify:

  • backend/guardrails/cost_tracker.py (new)
  • frontend/src/pages/Admin/CostGovernance.tsx (modify - enhance)

Phase 3: Advanced Capabilities (Weeks 9-12)

Layer 1: Interaction - Generative UI & HITL

Task 1.1: Generative UI Component System

Priority: High | Effort: 2 weeks | Dependencies: None

Sub-tasks:

  • 1.1.1 Component Schema System
    • Define Zod schemas for UI components
    • Create component registry (frontend/src/components/GenUI/registry.ts)
    • Implement typed components:
      • <DataTable /> - For tabular data
      • <Chart /> - For visualizations
      • <ApprovalCard /> - For HITL approvals
      • <Timeline /> - For workflow visualization
      • <Form /> - For structured inputs
  • 1.1.2 Agent Output Parser
    • Create parser for structured JSON payloads
    • Validate against Zod schemas
    • Render components dynamically
    • Handle fallback to markdown for unstructured output
  • 1.1.3 Component Library
    • Build reusable GenUI components
    • Add styling and animations
    • Test component rendering
    • Document component usage

Acceptance Criteria:

  • Agents can output structured UI payloads
  • Components render correctly from JSON
  • Fallback to markdown works seamlessly
  • Component library documented

Files to Create/Modify:

  • frontend/src/components/GenUI/registry.ts (new)
  • frontend/src/components/GenUI/DataTable.tsx (new)
  • frontend/src/components/GenUI/Chart.tsx (new)
  • frontend/src/components/Chat/ChatMessage.tsx (modify - add GenUI support)

Task 1.2: Advanced Streaming

Priority: Medium | Effort: 1 week | Dependencies: 1.1

Sub-tasks:

  • 1.2.1 Separate Streams for Text vs Structure
    • Create separate WebSocket channels for text and UI updates
    • Stream text tokens for conversational elements
    • Stream structural updates for UI components
    • Sync streams on frontend
  • 1.2.2 Progressive Rendering
    • Render charts/tables incrementally as data arrives
    • Show loading states for incomplete components
    • Add smooth transitions
  • 1.2.3 Optimistic UI Updates
    • Implement optimistic updates for form submissions
    • Rollback on error
    • Show pending states

Acceptance Criteria:

  • Text and UI streams work independently
  • Components render progressively
  • Optimistic updates improve perceived latency
  • Error handling graceful

Files to Create/Modify:

  • backend/api/routers/chat.py (modify - add separate streams)
  • frontend/src/hooks/useChatStream.ts (modify - handle dual streams)

Task 1.3: Complete HITL UI

Priority: High | Effort: 1 week | Dependencies: 1.1

Sub-tasks:

  • 1.3.1 Pending Approvals Component
    • Create frontend/src/pages/Workflows/PendingApprovals.tsx
    • List all workflows waiting for approval
    • Show approval context and proposed actions
    • Implement approve/reject/edit actions
  • 1.3.2 Parameter Editing UI
    • Create parameter editor component
    • Allow editing tool call parameters before execution
    • Validate edited parameters
    • Submit edited parameters to workflow
  • 1.3.3 Real-Time Workflow Status
    • Add WebSocket connection for workflow updates
    • Show live status in UI
    • Display current step and progress

Acceptance Criteria:

  • Users can see and approve pending workflows
  • Tool parameters editable before execution
  • Real-time workflow status visible
  • HITL flows complete end-to-end

Files to Create/Modify:

  • frontend/src/pages/Workflows/PendingApprovals.tsx (new)
  • frontend/src/components/Workflows/ParameterEditor.tsx (new)
  • frontend/src/pages/Workflows/ActiveWorkflows.tsx (modify - add real-time updates)

Layer 4: Memory - GraphRAG & Context Optimization

Task 4.1: GraphRAG Implementation

Priority: High | Effort: 2 weeks | Dependencies: None

Sub-tasks:

  • 4.1.1 Knowledge Graph Setup
    • Deploy Graphiti (Zep’s knowledge graph) or KuzuDB
    • Create entity extraction pipeline
    • Extract entities during document ingestion
    • Store relationships in graph
  • 4.1.2 Multi-Hop Traversal
    • Implement graph traversal queries
    • Support multi-hop relationship queries
    • Add graph query API endpoint
    • Integrate into memory search
  • 4.1.3 Hybrid Search
    • Add BM25 keyword search
    • Combine vector + keyword + graph search
    • Implement result fusion algorithm
    • Tune retrieval based on query type

Acceptance Criteria:

  • Knowledge graph stores entities and relationships
  • Multi-hop queries work correctly
  • Hybrid search improves retrieval accuracy
  • Graph queries integrated into agent memory

Files to Create/Modify:

  • backend/memory/graph.py (new)
  • backend/memory/client.py (modify - add graph search)
  • backend/api/routers/memory.py (modify - add graph endpoints)

Task 4.2: Context Optimization

Priority: Medium | Effort: 1 week | Dependencies: 4.1

Sub-tasks:

  • 4.2.1 Automatic Summarization
    • Implement summarization after N turns (e.g., 20)
    • Preserve key decisions and state changes
    • Store summaries in session metadata
    • Use summaries for context injection
  • 4.2.2 Rolling Window Pattern
    • Keep last N messages verbatim (e.g., 10)
    • Summarize older messages
    • Inject summary + recent messages into context
    • Implement anchor summarization for long conversations
  • 4.2.3 Context Trimming
    • Detect task boundaries
    • Remove completed task details from context
    • Keep only relevant context for current task
    • Monitor context window usage

Acceptance Criteria:

  • Summarization reduces context size by 60%+
  • Rolling window maintains recent context verbatim
  • Context trimming improves relevance
  • Context window usage optimized

Files to Create/Modify:

  • backend/memory/context_optimizer.py (new)
  • backend/agents/base.py (modify - use optimized context)

Phase 4: Enterprise Polish (Weeks 13-16)

Layer 2: Orchestration - Advanced Patterns

Task 2.1: Enhanced Self-Correction

Priority: Medium | Effort: 1 week | Dependencies: 3.3

Sub-tasks:

  • 2.1.1 Explicit ReAct Loop
    • Implement ReAct pattern in agent reasoning (from Layer 3)
    • Add tool output parsing with error detection
    • Enable retry with alternative strategy on failures
    • Log self-correction attempts
  • 2.1.2 Error Recovery
    • Detect tool execution failures
    • Analyze error messages
    • Generate alternative strategies
    • Retry with new approach

Acceptance Criteria:

  • ReAct loop visible in agent execution
  • Self-correction works on tool failures
  • Alternative strategies attempted automatically
  • Success rate improves by 10%+

Files to Create/Modify:

  • backend/agents/base.py (modify - enhance ReAct loop)

Task 2.2: Hierarchical Agent Planning

Priority: Medium | Effort: 1.5 weeks | Dependencies: 2.1

Sub-tasks:

  • 2.2.1 Planner Agent
    • Create PlannerAgent class
    • Decompose complex goals into milestones
    • Generate execution plan
    • Track milestone completion
  • 2.2.2 Milestone Tracking
    • Store milestones in workflow state
    • Update milestones as tasks complete
    • Display milestones in UI
    • Alert on milestone delays

Acceptance Criteria:

  • Planner agent creates execution plans
  • Milestones tracked and visible
  • Complex goals broken down correctly
  • Plan execution monitored

Files to Create/Modify:

  • backend/agents/planner.py (new)
  • frontend/src/components/Workflows/Milestones.tsx (new)

Task 2.3: State Persistence & Branching

Priority: High | Effort: 1.5 weeks | Dependencies: None

Sub-tasks:

  • 2.3.1 State Persistence
    • Migrate from in-memory dict to Redis/PostgreSQL
    • Store session state in database
    • Implement state serialization/deserialization
    • Add state versioning
  • 2.3.2 State Branching
    • Implement state forking for “what-if” scenarios
    • Create branch from current state
    • Execute alternative paths
    • Compare branch outcomes
    • Merge or discard branches
  • 2.3.3 Time Travel Debugging
    • Use Temporal history for state replay
    • Create UI for state inspection
    • Allow rewinding to previous states
    • Replay from any point

Acceptance Criteria:

  • State persists across restarts
  • State branching works for what-if scenarios
  • Time travel debugging functional
  • State versioning prevents data loss

Files to Create/Modify:

  • backend/orchestration/state_store.py (new)
  • backend/orchestration/state_branching.py (new)
  • frontend/src/pages/Workflows/StateInspector.tsx (new)

Layer 5: Tools - Sandboxing & Validation

Task 5.1: Sandboxed Code Execution

Priority: Medium | Effort: 2 weeks | Dependencies: 6.2 (policy engine)

Sub-tasks:

  • 5.1.1 E2B Integration
    • Set up E2B account and API keys
    • Create backend/tools/code_executor.py
    • Implement sandbox creation and destruction
    • Add code execution with timeouts
  • 5.1.2 Code Execution Tool
    • Create execute_code tool for agents
    • Support Python, JavaScript, SQL
    • Return execution results or errors
    • Add data analysis capabilities (CSV processing, calculations)
  • 5.1.3 Security & Isolation
    • Implement network isolation
    • Add execution timeouts (30s default)
    • Restrict file system access
    • Log all code executions

Acceptance Criteria:

  • Agents can execute code safely
  • Sandboxes isolated from host
  • Timeouts prevent infinite loops
  • All executions logged and auditable

Files to Create/Modify:

  • backend/tools/code_executor.py (new)
  • backend/agents/tools.py (modify - add execute_code)

Task 5.2: Tool Validation Middleware

Priority: High | Effort: 1 week | Dependencies: 5.1

Sub-tasks:

  • 5.2.1 Pre-Execution Validation
    • Create validation middleware layer
    • Validate tool parameters against schemas
    • Check parameter types and ranges
    • Generate structured errors on failure
  • 5.2.2 Agent Self-Healing
    • Feed validation errors back to agent
    • Allow agent to correct parameters
    • Retry with corrected parameters
    • Log self-healing attempts
  • 5.2.3 Parameter Sanitization
    • Sanitize all tool parameters
    • Prevent injection attacks
    • Validate file paths
    • Check resource limits

Acceptance Criteria:

  • All tool calls validated before execution
  • Validation errors trigger self-healing
  • Parameter sanitization prevents attacks
  • Self-healing success rate >80%

Files to Create/Modify:

  • backend/tools/validation.py (new)
  • backend/agents/base.py (modify - add validation middleware)

Implementation Timeline 

Week 1-4:   Phase 1 - Critical Security (Layer 6)
Week 5-8:   Phase 2 - Production Reliability (Layers 3, 7)
Week 9-12:  Phase 3 - Advanced Capabilities (Layers 1, 4)
Week 13-16: Phase 4 - Enterprise Polish (Layers 2, 5)

Dependencies Map 

Layer 6 (Guardrails) → All other layers (must be first)
Layer 3 (Cognition) → Layer 7 (Observability) - cost tracking
Layer 1 (Interaction) → Layer 2 (Orchestration) - HITL workflows
Layer 4 (Memory) → Layer 3 (Cognition) - context injection
Layer 5 (Tools) → Layer 6 (Guardrails) - policy enforcement

Success Metrics

Layer 6: Guardrails

  • ✅ 100% of inputs pass through guardrails
  • ✅ 0 prompt injection successes
  • ✅ 100% PII redaction rate
  • ✅ <1% false positive rate

Layer 3: Cognition

  • ✅ 40%+ cost reduction via smart routing
  • ✅ 99.9%+ uptime via fallback chains
  • ✅ <5% validation failure rate

Layer 7: Observability

  • ✅ 100% of executions traced
  • ✅ Golden dataset with 50+ test cases
  • ✅ Quality metrics tracked daily

Layer 1: Interaction

  • ✅ GenUI components render correctly
  • ✅ HITL approval time <5 minutes
  • ✅ Streaming latency <300ms

Layer 4: Memory

  • ✅ 60%+ context size reduction
  • ✅ Multi-hop queries work correctly
  • ✅ Hybrid search improves accuracy

Layer 2: Orchestration

  • ✅ State persists across restarts
  • ✅ Self-correction success rate >80%
  • ✅ Time travel debugging functional

Layer 5: Tools

  • ✅ Code execution isolated
  • ✅ Validation prevents 95%+ errors
  • ✅ Self-healing success rate >80%

Risk Mitigation

High-Risk Areas

  1. Layer 6 Implementation - Critical for security, must be done first
  2. State Migration - Risk of data loss during migration
  3. Cost Tracking - Must be accurate to prevent budget overruns

Mitigation Strategies

  • Implement Layer 6 in stages with testing at each stage
  • Use feature flags for gradual rollout
  • Backup state before migration
  • Monitor costs closely during implementation
  • Set up alerts for anomalies

Next Steps

  1. Review this plan with team
  2. Prioritize tasks based on business needs
  3. Assign owners for each task
  4. Set up project tracking (GitHub Projects, Jira, etc.)
  5. Begin Phase 1 - Critical Security (Layer 6)

Last Updated: December 20, 2024
Document Version: 1.0