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Roadmap

Shipped (Phase 1 + Phase 2)

Both phases are complete. The full foundation is solid.

  • 7 chapters covering definitions, tool design, workflow-vs-agent architecture, multi-agent systems, human-in-the-loop, evaluation and hardening, and the judgment chapter on when not to use agents
  • Working code for every concept: tool registry, context pipeline, agent loop, workflow implementation, bounded agent, state management, multi-agent orchestration, approval gates, escalation engine, audit logging, eval harness, tracer, reliability hardening, cost profiler, security hardening
  • 2 end-to-end projects: Document Intelligence Agent and Incident Runbook Agent
  • Eval harness with gold dataset, rubric, scored comparison script, and failure buckets
  • 52+ passing tests across unit and integration suites
  • 9 architecture-grade diagrams (hand-crafted SVGs)
  • MkDocs Material site at sunilprakash.com/agentic-ai
  • Infrastructure: pyproject.toml, Makefile, .env.example, MIT license

Next (Phase 3 -- Production Depth)

Phase 3 tackles what happens after you build the agent: deploying it, making it self-aware, governing it, and connecting it to other systems. Directions, not promises.

  • Metacognition and self-reflection -- agents that reason about their own reasoning: detecting loops, evaluating output quality, adjusting strategy mid-task, knowing when to stop vs when to try harder
  • Deployment and scaling -- how to run agent systems in production at scale: containerization, queue-based orchestration, autoscaling agent workers, latency budgets, infrastructure patterns for multi-step agent workloads
  • Deeper security chapter -- standalone treatment of prompt injection, tool abuse, data exfiltration, and policy enforcement at scale
  • Governance and auditability -- audit trails, decision logging, compliance boundaries, risk-tier enforcement across an organization
  • Bridge chapter -- how agent systems fit into enterprise operating models (connects to "The Enterprise AI Operating System")
  • 1-2 more projects: Codebase Analyst, Data Analyst Agent

Future (Phase 4 -- Advanced)

Phase 4 covers the harder problems that emerge once you have production agent systems running at scale.

  • Protocols and interoperability -- MCP, A2A, and how agent systems communicate across trust boundaries
  • Durable execution -- long-running agents, checkpointing, resume-after-failure at scale, event-sourced agent state
  • Advanced memory systems -- beyond session state: long-term memory, retrieval-augmented memory, memory distillation, forgetting, memory governance
  • Advanced planning -- tree search, iterative refinement, plan verification, plan-and-execute at scale
  • Evaluation at scale -- evaluating agent systems across hundreds of tasks, regression detection, continuous eval, synthetic dataset generation
  • Context engineering deep-dive -- advanced context assembly, token budget management, dynamic context prioritization, context compression strategies
  • Remaining projects: Policy Controls Agent, Multi-Agent Research System

Content ships when it meets the quality bar. No timelines promised.