Vision: The Future of AI Tooling in Decknix

The current agent shell is Layer 5 of a broader vision. Here's where it's heading.

Literate Session Export (Next)

Issue: decknix#55

Every AI session becomes publishable knowledge:

C-c E o    → Export to Org-mode
C-c E m    → Export to Markdown
C-c E h    → Export to HTML
C-c E c    → Export to Confluence (ADF)

Why this matters: An investigation session becomes a post-mortem. An architecture discussion becomes a design doc. A bug hunt becomes a runbook. The AI conversation is the documentation — export makes it shareable.

Role-Based Workflow Profiles

The agent shell currently serves a single persona: the developer writing code. But AI-assisted workflows extend far beyond coding.

Engineering Workflows

Workflow	What the Agent Does
Investigation	Query logs (GCP MCP), search knowledge base, correlate errors, produce root cause analysis
Architecture Review	Analyse codebase structure, identify coupling, suggest decomposition, generate ADRs
Incident Response	Real-time log tailing, alert correlation, runbook execution, post-mortem drafting
Code Review	Automated review on commit, PR summary generation, review thread resolution
Onboarding	Guided codebase exploration, convention explanation, first-task scaffolding

Transformative Engineering

Beyond individual developer productivity, the tooling enables transformative engineering — systematic, AI-assisted modernisation of large codebases:

Capability	Description
Migration planning	Analyse a legacy codebase, identify migration paths, estimate effort, generate step-by-step plans
Pattern extraction	Detect repeated patterns across services, propose shared libraries, generate extraction PRs
Observability gap analysis	Compare metric/alert coverage against error hierarchies, identify blind spots
Test coverage expansion	Analyse untested paths, generate test scaffolds, prioritise by risk
Cross-service coherence	Validate that API contracts, event schemas, and alert definitions stay consistent across services

Beyond Engineering

The same session-as-first-class-object model applies to non-engineering roles:

Role	Workflow
Product	Spec refinement sessions, user story generation, acceptance criteria drafting
QA	Test plan generation, exploratory testing guidance, regression analysis
Support	Ticket investigation with knowledge base search, escalation drafting
Leadership	Sprint retrospective analysis, technical debt quantification, roadmap impact assessment

Workflow Templates

Future slash commands and templates will be role-aware:

/investigate <property-id>     → Full NC property sync investigation
/incident <alert-name>         → Incident response runbook
/review-pr <PR-number>         → Structured code review
/onboard <repo-name>           → Guided codebase tour
/migrate <from> <to>           → Migration planning session

These would combine MCP server access, knowledge base search, and structured output into repeatable workflows.

Multi-Agent Orchestration

The session manager and attention tracker already support multiple concurrent sessions. The next step is coordinated multi-agent workflows:

Parallel investigation — spawn multiple agents to investigate different aspects of an incident simultaneously
Review pipeline — one agent reviews code, another checks test coverage, a third validates observability
Continuous monitoring — background agents that watch CI, alert channels, or deployment status and inject findings into active sessions

Declarative Workflow Definitions

Workflows as Nix configuration:

{ ... }: {
  decknix.ai.workflows = {
    investigate = {
      description = "Full property sync investigation";
      mcpServers = [ "gcp-monitoring" "org-knowledge-base" ];
      template = "investigate";
      context.autoPin = [ "jira" ];  # Auto-pin Jira tickets mentioned
    };
    incident = {
      description = "Incident response runbook";
      mcpServers = [ "gcp-monitoring" "pagerduty" ];
      template = "incident";
      attention.priority = "high";  # Always show in attention tracker
    };
  };
}

The Endgame

The vision is an environment where:

Every AI conversation produces artefacts — not just code changes, but documentation, decisions, and knowledge
Workflows are reproducible — a new team member gets the same investigation tools, templates, and MCP access as a senior engineer
Context is continuous — switching between sessions preserves the full picture of what you're working on
The tooling adapts to the role — engineers, product managers, and support staff each get workflows tailored to their needs
The environment is declarative — decknix switch reproduces the entire AI-assisted workflow on any machine

Decknix Documentation