How to Build a Self-Improving Agentic RAG System

Manually tuning an Agentic RAG system is painful: you tweak prompts, juggle retrieval settings, add new agents, and still end up with hallucinations and brittle behavior. As of November 2025, the state of the art has shifted toward self-improving RAG: architectures where specialist agents, systematic evaluation, and continuous feedback loops allow the system to adapt on its own. In this guide, you’ll learn how to design and implement a self-improving Agentic RAG pipeline inspired by recent Agentic RAG patterns, multi-agent routing research, and modern evaluation frameworks like TruLens, RAGAS, and ARES (2024–2025).

We’ll walk through the architecture, define specialist agents, set up multi-dimensional evaluation, and wire up a feedback loop so your RAG system can detect failures, learn from them, and improve without constant human babysitting.

What is a self-improving Agentic RAG system?

Retrieval Augmented Generation (RAG) pairs an LLM with a retriever (typically a vector database) so the model can answer questions using fresh, domain-specific data instead of relying only on pretraining. Agentic RAG extends this with AI agents that can plan, choose tools, call sub-agents, and iterate on answers, giving you workflows like multi-step reasoning, tool use, and decision-making.

A self-improving Agentic RAG system adds a third layer:

• It monitors itself via multi-dimensional evaluations (retrieval quality, grounding, answer relevance, latency, cost).
• It routes work through specialist agents that can diagnose and repair failures (e.g., retriever-tuning agents, prompt-rewriting agents).
• It updates configuration, prompts, and sometimes data automatically over time using logged interactions and evaluations.

The result is a RAG pipeline that behaves more like an autonomous product team: one agent answers questions, others audit, another improves retrieval, another tunes prompts or routes queries to new skills.

High-level architecture of a self-improving Agentic RAG

At a high level, you can think of five layers:

1. Entry router: Classifies and routes user queries to the right specialist agents or pipelines.
2. Core Agentic RAG pipeline: Planner, retriever, and answer generator.
3. Evaluation layer: Automated metrics and LLM-based evaluators (e.g., TruLens RAG triad, RAGAS, ARES).
4. Improvement agents: Agents that modify prompts, retriever settings, routing strategies, or knowledge base.
5. Feedback store & orchestration: Logs, metrics database, and a scheduler to trigger improvement workflows.

You can implement this with modern frameworks such as:

• LangChain 1.0+ (Python/TypeScript, v1.0.8 on PyPI as of November 19, 2025) and LangGraph 1.0 for agent workflows.
• Haystack 2.20+ (release notes November 13, 2025) for production-ready RAG and agents.
• LLMs like GPT-4o (OpenAI’s main GPT-4 class multimodal model in 2025), Claude Sonnet 4.5 (Anthropic’s flagship balanced model released Sep 29, 2025), or open-weight Llama 4 models (Meta, April 2025) as your reasoning engines.

Designing specialist agents for your Agentic RAG

Agentic RAG is most powerful when you decompose responsibilities into specialist agents rather than one monolithic “mega-agent.” Recent multi-agent routing work (e.g., RopMura 2025) and enterprise Agentic RAG case studies show better robustness and interpretability with clear specialization.

Core agents you should define

• Request router agent
  Classifies queries and decides which downstream pipeline to use:
  • Simple FAQ RAG
  • Deep research RAG (multi-hop)
  • Tool-using agent (e.g., call APIs, run code)
  • Fallback direct LLM (no retrieval)
• Planner agent
  Breaks complex questions into sub-steps (e.g., “summarize document then compare two options”). It orchestrates calls to retrievers, tools, and other agents.
• Retriever agent
  Encapsulates:
  • Index selection (vector store vs. keyword vs. hybrid)
  • Query rewriting (e.g., HyDE-style hypothetical docs)
  • Dynamic k (top-k) and filter tuning
• Answering agent
  Builds the final response from retrieved context, applying instructions, style, and safety rules.
• Critic / evaluator agent
  Rates responses on factuality, grounding, clarity, and completeness. Optionally produces a revised answer.
• Improvement agents
  Triggered offline or asynchronously to adjust:
  • Prompts (system messages, chain-of-thought patterns)
  • Retriever configuration (similarity threshold, top-k)
  • Routing rules and agent selection boundaries
  • Index contents (detect missing documents, stale data)

Example: implementing agents with LangChain 1.0

from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_tool_calling_agent
from langchain_core.tools import Tool

llm = ChatOpenAI(model="gpt-4o")  # As of 2025, GPT-4o is the main GPT-4 class model

def retrieve_docs(query: str) -> str:
    # Call your vector DB here, return concatenated context
    ...

def eval_answer(inputs: dict) -> dict:
    # LLM-based evaluator: rate groundedness, relevance, etc.
    ...

retriever_tool = Tool(
    name="retriever",
    func=retrieve_docs,
    description="Retrieve relevant documents for a user question."
)

evaluator_tool = Tool(
    name="evaluator",
    func=eval_answer,
    description="Evaluate groundedness and relevance of an answer."
)

system_prompt = """
You are the Answering Agent in an Agentic RAG system.
Use the `retriever` tool to fetch context before answering.
Never answer without citing retrieved evidence.
"""

answering_agent = create_tool_calling_agent(
    llm=llm,
    tools=[retriever_tool],
    prompt=system_prompt,
)

answering_executor = AgentExecutor(
    agent=answering_agent,
    tools=[retriever_tool],
    verbose=True,
)

In practice, you define similar agents for routing, planning, and improvement and wire them together using LangGraph, Haystack workflows, or a custom orchestrator.

Building a multi-dimensional evaluation system

Self-improvement is impossible without rich, automated feedback. Recent evaluation tools and research (TruLens RAG triad, RAGAS, ARES 2024, EncouRAGe 2025) show that single metrics like “BLEU” or “exact match” are not enough for RAG.

Design your evaluation around four key dimensions:

• Retrieval quality: Are the retrieved documents relevant to the query?
• Groundedness: Is the answer supported by retrieved content, or hallucinated?
• Answer quality: Relevance, completeness, coherence, style.
• System performance: Latency, cost, and robustness across query types.

Instrumenting your RAG pipeline

Use an evaluation library to wrap your RAG calls and log traces. For example, TruLens (RAG Triad, updated through 2024–2025) lets you define evaluators for context relevance, groundedness, and answer relevance using LLM-judged scores.

from trulens_eval import TruChain, Feedback
from trulens_eval.feedback.provider.openai import OpenAI

provider = OpenAI(model_engine="gpt-4o")

f_context_relevance = Feedback(
    provider.relevance, name="context_relevance"
).on_input_output()

f_groundedness = Feedback(
    provider.groundedness, name="groundedness"
).on_input_output()

tru_answering = TruChain(
    chain=my_rag_chain,  # your LangChain or Haystack pipeline
    feedbacks=[f_context_relevance, f_groundedness],
)

# When serving traffic:
with tru_answering as recording:
    result = my_rag_chain.invoke({"question": user_query})

Store these metrics plus raw prompts, retrieved docs, and answers in a database or observability tool (e.g., LangSmith, Haystack telemetry, or your own ClickHouse/Postgres). These logs become the fuel for self-improvement.

Implementing the self-improvement feedback loop

With agents and evaluation in place, the key innovation is a loop that takes failures and updates the system automatically. Borrowing from active learning and SimRAG-style self-improvement research, you can design a loop like this:

1. Collect interactions: For each query, store inputs, retrieved docs, answers, evaluation scores, and user feedback (thumbs up/down, edits).
2. Detect failure patterns: Periodically run an analyzer agent on the logs to cluster and label failure modes (e.g., “missing policy documents,” “ambiguous query,” “hallucinated product specs”).
3. Trigger targeted improvement agents based on failure type.
4. Apply controlled updates: Adjust prompts, retriever params, routing rules, or data indexes and roll them out behind feature flags.
5. Re-evaluate: Compare metrics before and after changes on held-out evaluation sets and live traffic.

Example: automatic prompt improvement loop

Suppose your groundedness scores drop for “legal policy” queries. Here’s how a prompt-improvement agent could operate:

1. Select all low-groundedness interactions tagged as legal.
2. Feed them into a Prompt Tuner Agent with context (current system prompt, few good examples, failure examples).
3. Ask it to propose prompt edits and new exemplars.
4. Run A/B tests offline on a curated evaluation set, using the same multi-dimensional metrics.
5. Deploy improved prompt to production if it wins.

def improve_prompt(failed_examples, current_prompt):
    system = """
    You are a Prompt Tuning Agent for an Agentic RAG system.
    Given the current system prompt and failed examples,
    propose an improved prompt plus 3 high-quality exemplars.
    """
    messages = [
        {"role": "system", "content": system},
        {"role": "user", "content": f"Current prompt:\n{current_prompt}"},
        {"role": "user", "content": f"Failed examples:\n{failed_examples}"}
    ]
    resp = llm.chat.completions.create(
        model="gpt-4o",
        messages=messages,
    )
    return resp.choices[0].message.content

This agent can run nightly, generate candidate prompts, and push them into a configuration store for offline evaluation.

Example: retriever self-tuning agent

Similarly, a Retriever Tuning Agent can:

• Analyze low context-relevance scores and identify query patterns.
• Suggest changes to:
  • top_k and similarity thresholds,
  • hybrid (vector + BM25) vs. pure vector retrieval,
  • domain-specific filters (e.g., language, date range).
• Run experiments on a labeled test set and update retriever config when metrics improve.

Putting it all together: a step-by-step implementation plan

Step 1: start with a solid vanilla RAG

1. Choose an LLM (e.g., GPT-4o, Claude Sonnet 4.5, or Llama 4 Scout for open weights).
2. Build a basic RAG pipeline with:
   • Document loader and chunking
   • Embedding model and vector store
   • Simple retriever (similarity_search)
   • LLM answer generator with context injection
3. Keep this as your baseline pipeline.

Step 2: add agentic control (router + planner)

1. Add a Router Agent that:
   • Classifies intents (FAQ vs. deep research vs. tool call)
   • Chooses between baseline RAG, advanced RAG, or direct LLM
2. Add a Planner Agent for complex workflows:
   • Break questions into sub-tasks
   • Call retriever multiple times
   • Perform comparisons and aggregations
3. Use LangGraph or Haystack components to define explicit state machines rather than unstructured tool chatter.

Step 3: instrument evaluation from day one

1. Wrap your pipelines with TruLens or RAGAS to compute:
   • Context relevance
   • Groundedness / faithfulness
   • Answer relevance
2. Log metrics, prompts, retrieved docs, and user feedback into a centralized store.
3. Build dashboards (e.g., using LangSmith or your own BI tool) to monitor trends by:
   • Query type
   • Tenant or project
   • Agent route used

Step 4: build first improvement agents

1. Implement a Prompt Tuning Agent to update system prompts for specific domains (legal, support, finance).
2. Implement a Retriever Tuning Agent to optimize:
   • Embedding choice (e.g., switching to domain-tuned models)
   • top-k and filters
   • Hybrid vs. dense-only retrieval
3. Add a Knowledge Gap Agent that:
   • Detects answers that refer to unknown entities or out-of-date info
   • Flags missing documents and suggests new ingestion tasks

Step 5: automate the feedback loop with guardrails

1. Schedule batch jobs (cron, Airflow, or LangGraph/Haystack workflows) to:
   • Sample recent interactions
   • Run failure analysis and improvement agents
   • Generate candidate updates
2. Enforce guardrails:
   • All changes must pass offline evaluation on a fixed benchmark set.
   • Use feature flags and gradual rollouts (e.g., 10% of traffic).
   • Auto-rollback if metrics regress.
3. Keep humans “in the loop” at policy-critical points (e.g., regulated domains) via approval dashboards.

Conclusion: from static RAG to living Agentic systems

Static RAG pipelines quickly degrade as your content, users, and models evolve. A self-improving Agentic RAG system turns RAG into a living system: specialist agents handle routing, retrieval, and answering; evaluation layers continuously measure retrieval quality, groundedness, and answer relevance; and improvement agents close the loop by tuning prompts, retrievers, and routing strategies based on real-world performance. Using current tooling such as LangChain 1.0, Haystack 2.20, TruLens, RAGAS, and state-of-the-art models like GPT-4o, Claude Sonnet 4.5, or Llama 4, you can implement this architecture today.

Your next steps: start with a robust baseline RAG, instrument it with multi-dimensional evaluation, then gradually introduce agents for routing, planning, and self-improvement. Over time, you’ll move from hand-tuning prompts and k-values to managing an autonomous, metrics-driven Agentic RAG system that keeps getting better with every interaction.