Agents & Autonomy - Roadmap

🤖 1. Core Foundations of Agentic Systems

1.1: The Agentic Paradigm Shift — From Static Models to Dynamic Reasoners

1. Understand the philosophical shift: traditional LLMs respond reactively; agents act proactively, chaining reasoning + actions + learning.

2. Study the ReAct Framework — the foundation of reasoning-action loops.

   • Paper: “ReAct: Synergizing Reasoning and Acting in Language Models.”
   • Implementation: Build a ReAct loop with OpenAI/Claude API using scratch Python — reasoning, parsing, and API tool invocation.

3. Learn the distinction between:

   • Cognitive Loop (reason → act → observe → reflect)
   • Execution Loop (plan → tool → validate → retry)

Deeper Insight: Interviewers will probe: “Why does ReAct outperform Chain-of-Thought prompting in tool-based tasks?” Be ready to discuss feedback integration and action grounding. Bonus question: “How do you prevent infinite reasoning loops?” — hint: token budgeting, confidence heuristics, or reflection scoring.

1.2: From ReAct to AutoGPT, BabyAGI, and Modern Agentic Architectures

1. Explore AutoGPT, BabyAGI, and AgentGPT as early “looped autonomy” frameworks.

2. Understand task decomposition, recursive planning, and memory persistence in these systems.

3. Build your own “AutoGPT-Lite”:

   • Create a task loop with goals.json, memory.txt, and a basic executor.
   • Let the agent reflect on progress and self-correct using summaries.

Deeper Insight: Interview probing: “What are the design flaws of early AutoGPT?” Discuss instability, hallucination propagation, and lack of contextual grounding — and how structured memory or graph-based planning solves them.

1.3: Agentic Architectures — Modular Reasoning Systems

1. Study Toolformer and Code Interpreter (Python REPL) as examples of modular agent design.

2. Understand the pipeline:

   User Query → Intent Parser → Planner → Tool Selector → Execution Engine → Reflection

3. Learn how Toolformer automates the insertion of tool-use tokens, enabling API self-invocation.

4. Build a simple tool-calling agent using OpenAI’s function calling or JSON mode API.

Probing Question: “When should an agent invoke a tool vs. reason internally?” Discuss trade-offs: external cost vs. accuracy, API latency vs. reasoning clarity. At scale, mention tool usage caching and structured schema enforcement for robustness.

🧠 2. Memory, Planning & Control (MCP)

2.1: Memory Systems — Short-Term vs Long-Term Context

1. Study short-term context management (token-level recall) vs long-term vector memory (semantic retrieval).

2. Implement memory using:

   • faiss or chromadb for vector storage.
   • A retrieval policy: “When should I recall vs. requery?”

3. Explore episodic vs semantic memory in agent frameworks.

4. Integrate LangGraph or CrewAI memory modules into a ReAct loop.

Deeper Insight: Interviewers may ask: “How would you avoid memory drift in long-running agents?” Discuss summarization compression, forgetting mechanisms, and relevance decay. Mention vector store re-indexing for performance consistency.

2.2: Planning Systems — Goal Decomposition & Reflection

1. Learn Hierarchical Task Networks (HTNs) and Tree-of-Thoughts (ToT) for structured planning.
2. Implement a goal tree builder: recursively break complex objectives into atomic subtasks.
3. Integrate a reflection module: post-task summarization → plan refinement.
4. Simulate agent self-reflection: “What worked? What didn’t?” using prompt-based scoring.

Probing Question: “How would you prevent recursive explosion in ToT search?” Explain pruning heuristics (e.g., beam search, utility thresholds). Top engineers discuss computational efficiency in large action spaces.

2.3: Control Systems — Self-Correction and Adaptive Feedback

1. Understand the PID analogy: agents as control systems minimizing error between goal and observation.
2. Implement feedback-based correction: measure delta between expected vs observed results.
3. Learn self-verification loops using logit-level reasoning or response evaluators.
4. Explore guardrails frameworks like GuardrailsAI and OpenDevin Control Graphs.

Deeper Insight: Expect questions like: “How would you prevent hallucination cascades in multi-step reasoning?” Discuss confidence gating, post-hoc verification (e.g., consistency scoring), and retrieval-based validation.

🧩 3. Multi-Agent Collaboration & Orchestration

3.1: Multi-Agent Collaboration

1. Understand role-based decomposition (planner, executor, reviewer, memory agent).

2. Study communication protocols:

   • Shared memory
   • Pub-sub via message queues (e.g., Redis Streams)
   • Contextual negotiation via LLM prompts

3. Implement a two-agent collaboration — one plans, another executes and critiques.

Deeper Insight: Probing question: “How do you prevent infinite debates or echo loops?” Introduce arbitration policies (e.g., round limits, consensus voting, role weighting).

3.2: LangGraph, CrewAI & Reliable Orchestration

1. Learn LangGraph (state-machine based orchestration for LLM agents).

2. Study CrewAI for modular team-based workflows — role-based collaboration, goal alignment, shared memory.

3. Understand graph-based execution DAGs:

   • Nodes = agents/tools
   • Edges = communication or task flow

4. Implement your own LangGraph clone using Python coroutines or asyncio.

Deeper Insight: “What’s the biggest challenge in agent orchestration?” Discuss state synchronization, race conditions, and determinism in concurrent message passing.

🧪 4. Evaluation & Benchmarking

4.1: Task Evaluation

1. Study benchmarks like SWE-Bench, WebArena, ToolBench, and GAIA.

2. Learn evaluation metrics:

   • Task success rate (TSR)
   • Reasoning consistency (RC)
   • Reflection gain (RG)
   • Tool efficiency (TE)

3. Implement custom eval pipelines: log tool calls, reasoning trace, and success/failure classification.

Deeper Insight: “How would you evaluate a self-evolving agent?” Discuss closed-loop evaluation — agents proposing new test cases, measuring self-improvement, and generating continuous learning curves.