Agentic AI + RAG: systems that think, read, and act

Most apps that "use AI" still behave like calculators: you ask, they answer. Agentic AI flips the flow. An agent senses a situation, forms a plan, calls tools and services, reads the right context, and iterates until a goal is met. Add RAG, and the agent gains a working memory of your corpus so it answers from facts, not vibes. (NVIDIA Blog)

TL;DR: Pair a planning loop with typed tool calls, a retrieval stack that you can measure, and strict safety boundaries. Ship small goals first, then grow autonomy.

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1) What makes an AI "agentic"?

An agentic system wraps a language model with four capabilities:

1. Perception: read user input, events, and documents.
2. Planning: break a goal into steps, choose tools, and order them.
3. Action: invoke typed tools and external systems; observe results.
4. Learning: write useful facts to memory and adjust the next step.

This is different from a chat UI. It is a control loop that keeps running until a termination condition is met or a human says stop. In practice, the loop is implemented as a task graph or state machine rather than a single monolithic prompt. (NVIDIA Blog, LangChain Blog)

Why now

Frontier models handle language, but tools and structured environments deliver reliability. You can run the planner with a small controller model and reserve big models for hard parts. This reduces cost and latency while increasing predictability. (arXiv)

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2) Tool use: the agent’s hands

Agents act via typed functions. The model emits a JSON call that matches a schema; your runtime executes it and streams back the result for the next reasoning step.

Design rules

• Schema first: name, description, parameters with types and enums. Treat it like a public API.
• Idempotence: make calls safe to retry; include request_id inputs and detect duplicates.
• Tight scopes: one tool per capability; avoid "doEverything()".
• Observability: log inputs, outputs, latency, and errors; attach the log to the agent’s next turn.

Function calling exists so models return structured calls reliably, not free-text that you must parse. Use it. (OpenAI, OpenAI Platform)

Minimal tool schema

json
{
  "type": "function",
  "name": "search_tickets",
  "description": "Search support tickets by query and status.",
  "parameters": {
    "type": "object",
    "properties": {
      "query": {"type": "string"},
      "status": {"type": "string", "enum": ["open", "closed", "all"]},
      "limit": {"type": "integer", "minimum": 1, "maximum": 100}
    },
    "required": ["query"],
    "additionalProperties": false
  },
  "strict": true
}

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3) Orchestration: from loops to graphs

Real tasks branch, fail, and retry. Model it explicitly:

• Finite-state machines for straight pipelines.
• Graphs for multi-agent or tool-rich flows where nodes can run in parallel and pass state.

Graph frameworks make step boundaries explicit, persist state, and let you recover mid-run. You can encode policies like "after three tool errors, escalate to human" or "if retrieval confidence is low, re-plan." (LangChain Blog, IBM)

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4) Retrieval-Augmented Generation (RAG): the agent’s library

RAG adds a fast, measurable knowledge path:

1. Chunking: split documents into self-contained pieces.
2. Embedding: map chunks to vectors for semantic search.
3. Retrieval: fetch candidates with vector search or hybrid search.
4. Re-ranking: use a cross-encoder to order candidates.
5. Grounded generation: answer with citations to the retrieved text.

This structure is now the de facto pattern. It reduces hallucinations and lets you control freshness and access. (arXiv, Microsoft Learn)

Practical choices that matter

• Chunking: prefer semantic or heading-aware splits; overlap 10–20% to preserve context windows.
• Hybrid retrieval: combine vector and BM25 for misspellings and rare terms.
• Re-ranking: re-score the top-k with a cross-encoder; move the best 5–20 into the prompt.
• Query rewriting: expand or decompose the user query before retrieval.
• Freshness: index docs continuously and version the index to enable rollbacks.

Re-ranking is the workhorse for quality jumps in enterprise RAG because it fixes coarse vector recall with a precise pairwise scorer. (arXiv, ragflow.io)

A compact RAG call the agent can reuse

ts
interface RetrievalRequest {
  query: string
  topK?: number // 50 for recall, then re-rank
  filters?: Record<string, string | number | boolean>
}

interface RetrievedDoc {
  id: string
  text: string
  source: string
  score: number
}

// 1) dense+BM25 -> 2) re-rank -> 3) pack

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5) Making agents and RAG work together

Loop sketch

pseudo
while not done:
  plan = planner(state)
  if plan.needs_context:
    ctx = retrieve(plan.query)
    state.context = ctx
  result = act(plan.tool, plan.args)
  observe(result)
  done = goal_check(state)

Key integration points

• Let the planner ask for retrieval explicitly. This keeps tool calls and context under budget.
• Cache retrieval results by (query_hash, filters, corpus_version).
• When confidence is low, branch: re-retrieve → re-rank → decompose question. Handle this as graph nodes, not prompt hacks. (LangChain Blog)

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6) Reliability, safety, and guardrails

Agentic systems can chain mistakes. Bound the blast radius:

• Zero trust for tools: narrow scopes, per-tool credentials, allow-lists, rate limits.
• Prompt-injection defense: never execute instructions found in retrieved content; treat corpus as untrusted input.
• Human-in-the-loop: require approvals for state-changing actions.
• Policy as code: encode stop conditions and approvals in the graph, not in prose prompts.

These are classical security controls adapted to agents. The solutions are mostly operational discipline plus visibility, not exotic research. (TechRadar)

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7) Measurement: what to track and why

You cannot improve what you cannot see. Track:

• Retrieval: context precision/recall, MRR/NDCG, coverage by document type.
• Answer quality: faithfulness to sources, grounded citation rate, factuality audits.
• Run health: tool error rates, retries, mean/percentile latencies, token and cash budgets.

Recent surveys catalogue RAG-specific metrics and datasets; borrow their checklists when you build your eval harness. (arXiv)

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8) Latency and cost engineering

• Use a small controller model for planning and tool selection; invoke larger models only for hard generations.
• Pre-warm connections to critical tools. Parallelize independent nodes.
• Shard the index by business unit; route queries to the smallest viable corpus.
• Memoize deterministic steps and paginate long traces to storage. (arXiv)

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9) Where agents + RAG already fit

• Customer support: triage → retrieve policy → propose action → file ticket updates.
• Internal ops: reconcile data across systems, draft change requests, open PRs with diffs.
• Research assistants: map a question into sub-questions, retrieve, and produce a brief with links.

Large players are publicly investing in agents for multi-step, high-reliability workflows, not just chat. Expect the stack around planning, tools, and sims to mature fast. (The Verge)

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10) A starter blueprint you can implement this week

1. Define goals as typed events: "answer_question", "draft_update", "file_change".
2. Register tools with strict JSON schemas and idempotent behavior.
3. Stand up RAG with hybrid retrieval and re-ranking. Version the index.
4. Build a graph: nodes for plan, retrieve, act, review, stop. Persist state per run.
5. Add guardrails: approval gates for state changes; redact secrets from logs.
6. Instrument: structured logs per node, per-tool SLAs, and weekly eval runs.

If you do just these, you’ll have a dependable agent that thinks, reads, and acts—on your terms.