Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
An AI-native platform is a governed set of reusable capabilities, not a language model behind an API with a vector database attached. Retrieval-augmented generation (RAG) is the request path that grounds answers in your own data. Orchestration is the control layer that sequences work when a task needs more than one step. Agentic patterns are what you get when the model itself chooses the next step, including whether to retrieve, which tool to call, and when it has enough information to answer. The architecture decisions are which of these your workload actually needs and which production controls each one requires.
The capabilities a platform has to govern
A complete account of an AI-native platform connects the following concerns. Each can be changed or scaled on its own, which is why the boundaries between them matter more than any single product.
- Model access: which models are called, how they are versioned, and how requests are routed to them.
- Data ingestion and retrieval: how sources are parsed, chunked, embedded, and searched.
- Orchestration: the logic that decides which steps run and in what sequence.
- Tool execution: the functions and APIs the system can invoke, and what those calls are allowed to change.
- State and memory: session context, persistent memory, and records of actions taken.
- Evaluation: how output quality and task outcomes are measured over time.
- Observability: traces of model calls, retrievals, and tool actions.
- Security: identity, permissions, and data protection across all of the above.
- Deployment: where each component runs and who operates it.
The cloud reference architectures discussed below are vendor-specific implementations of these concerns. They are useful for seeing how the pieces fit together, but none of them is a universal blueprint.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →The RAG request path, step by step
Google Cloud’s reference architecture RAG infrastructure for generative AI using Agent Platform and AlloyDB for PostgreSQL (last reviewed February 4, 2026) is a clear worked example. It separates ingestion from serving and treats evaluation as its own subsystem. Read it as one vendor’s design rather than the required shape of RAG.
#1 Best Overall
- Ingest. Sources can include files, databases, and streams. The pipeline parses the raw data, formats it, splits it into chunks, and generates an embedding for each chunk. Ingestion runs as a pipeline separate from serving, so changes to source documents reach the index through ingestion rather than through the request path.
- Index. The reference stores the embeddings in PostgreSQL using the
pgvectorextension. The application must use the same embedding model and parameters for source documents and for user requests. If they differ, query vectors and document vectors no longer sit in a comparable space, and similarity scores stop meaning much. Treat the embedding model as a versioned dependency, because changing it means re-embedding the corpus. - Retrieve and augment. At request time, the serving application embeds the user’s question, runs a semantic search, and combines the retrieved source content with the question to build a contextualized prompt.
- Generate and screen. The LLM answers using the supplied context, and the application screens the response before returning it. The reference presents this as its intended flow. Supplying context reduces errors but does not eliminate them, which is why the screening step and evaluation remain necessary.
- Evaluate. A separate subsystem scores responses on measures such as factual accuracy and relevance. Evaluation runs continuously as the corpus, prompts, and models change, not only before launch.
Where orchestration enters the request path
A plain RAG path has a fixed sequence written into application code: embed, retrieve, build the prompt, generate, screen. Orchestration becomes necessary when that sequence has to branch. A question may need a database lookup, a ticket lookup, a second retrieval against a different index, or a model call that rewrites the question first. An orchestration layer determines which tools are used, in what order, and how their outputs feed the next step.
AWS’s definitions in the Agentic AI Lens separate three shapes: a single agent using multiple tools, specialized agents coordinated together, and hybrid systems that combine agents with conventional software. Many production systems fall into the hybrid case, with fixed steps surrounding a few model-driven decisions.
Static retrieval versus agent-controlled retrieval
This is the point where RAG becomes agentic RAG. AWS’s definitions describe agentic RAG as retrieval becoming an action inside the reasoning loop. The agent may retrieve iteratively, decompose a question into sub-queries, choose among retrieval tools, and judge whether its context is sufficient before answering.
| Decision point | Static retrieval in a RAG request path | Agent-controlled (agentic) retrieval |
|---|---|---|
| Who decides to retrieve | Application code, on every request | The agent, which may skip or repeat retrieval |
| Query handling | One embedded query | The agent may decompose a question into sub-queries |
| Retrieval tools | One configured index or search path | The agent selects among available retrieval tools |
| Sufficiency check | None built into the path | The agent judges whether retrieved context is sufficient |
| Predictability | Same path on every run | Path varies by run and by model behavior |
| Model calls and latency | A fixed number of retrieval and generation calls per request | Multiple model calls and retrievals per request, each adding latency and cost |
| Evaluation | Score retrieval and answers against a fixed path | Score the path the agent took as well as the final answer |
Start with static retrieval. Move to agent-controlled retrieval when you can name the questions the static path answers poorly, such as multi-part questions that need separate lookups, and when you can measure that failure.
Choosing retrieval storage and deployment
The vector store is one component of retrieval, not the architecture. Google Cloud’s Generative AI with RAG architecture index (reviewed September 22, 2025) describes several routes. The right one depends on where your operational data lives and how much infrastructure your team is willing to run.
| Option | Where the reviewed sources place it | Choose it when | Check before committing |
|---|---|---|---|
| Managed vector search | Described in the Google Cloud RAG architecture index as a managed service | You want the provider to run and scale the vector store | You accept less control over internals. Confirm service limits, regions, and integration with your data systems for your workload. |
| PostgreSQL with vector support | PostgreSQL with pgvector in the AlloyDB reference architecture, alongside operational data |
Embeddings and business records should live in one database, and your team already operates PostgreSQL | Vector queries share resources with transactional queries, so capacity planning must cover both workloads. |
| Container-based open-source route | Open-source components deployed in containers | You need control over the stack and can absorb the operating burden | You own upgrades, scaling, backups, and failover. |
| Graph plus vector retrieval | Described in the Google Cloud RAG architecture index as combined vector and graph retrieval | Questions depend on relationships between entities, such as dependencies or ownership | Building and keeping the graph current requires additional modelling and ingestion work. |
Orchestration and agentic patterns
AWS’s pattern guide, Agentic AI patterns and workflows on AWS by Aaron Sempf and Andrew Hooker, covers agent patterns, LLM workflow patterns, and multi-agent patterns. The five below are the choices an architect most often weighs.
Rank #3
Tool-using agent
The model is given a set of authorized tools and chooses among them as it works toward a goal. The architectural weight falls on the permission boundary: which tools exist for this agent, what arguments it may pass, and how tool output is returned to the model. Tool output becomes input to the next model decision, so an unexpected response from one tool can change the entire path. Treat tool results as untrusted input.
Workflow orchestrator
A control component holds the sequence of steps and combines their results. The model may handle individual steps, but the flow itself is defined in advance. This suits teams that need an inspectable, repeatable process and want to know before a run which steps can occur.
Delegation and supervisor-worker
A coordinating agent assigns subtasks to specialist agents or roles and assembles their results. It can separate concerns cleanly, for example one agent for retrieval, one for calculation, and one for drafting. Each handoff adds latency, requires a message format that stays stable, and creates a point where context can be lost or misread.
Event-based coordination
Agents or services react to events rather than calling each other directly. AWS presents this as part of a broader cloud-native workflow. It decouples producers from consumers, but a single run’s path is then spread across events, so tracing has to follow event identifiers across services.
Agentic RAG
Retrieval becomes one action the agent can take. The retrieval design questions are covered in the comparison above, so the pattern-level question is whether one agent owns the retrieval loop or whether a dedicated retrieval component does.
Crashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minutePC Slower Than It Used to Be?
A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Choosing a pattern for the work
| Pattern | Best fit | Main cost | What to trace |
|---|---|---|---|
| Single agent with tools | Open-ended tasks where steps cannot be listed in advance, with a small toolset | Less predictable paths; every run must be reconstructed from its decisions | Each model decision and tool call, with its inputs and outputs |
| Workflow orchestration | Known, repeatable steps with a few model-driven decisions | Model flexibility is limited to the branches you designed | The defined flow and the output of each step |
| Delegated or collaborative agents | Separable subtasks that benefit from specialist roles | Coordination overhead, handoff complexity, and distributed failure modes | Activity across agents, including each handoff |
More agents is not automatically better architecture. AWS’s Well-Architected guidance names coordination overhead, handoff complexity, and distributed failure modes as design concerns for multi-agent systems. Where the steps are known and repeatable, a simpler workflow can be the better fit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Production controls when software can act
AWS’s Well-Architected Agentic AI Lens (revision dated June 10, 2026) frames the production question this way: “Organizations deploying agentic AI are moving from asking “can we build an agent?” to “can we run agents reliably, securely, and cost-effectively at scale?”” The lens treats autonomy, stochastic behavior, persistent memory, and agent collaboration as distinct architecture concerns. It also notes that one request can involve several model calls and tool invocations, each adding latency, cost, and failure surface.
- Scope and permissions. Give each agent an explicit tool list and a distinct identity with least-privilege access. Shared service credentials make it impossible to tell which agent performed an action.
- Human review matched to risk. Let read-only actions run unattended. Route irreversible actions, or actions that affect money, customers, or access rights, through an approval step, and set the threshold by how hard the action would be to undo.
- Traces that reconstruct a run. Record each model call, retrieval, and tool invocation, with its inputs, outputs, and the identity used, plus the reason the agent chose its next step. An operator should be able to replay what happened.
- Outcome-based evaluation. Test whether tasks complete correctly across repeated runs. Deterministic unit tests catch wiring errors but cannot show how a model behaves across varied inputs.
- Degradation rules. Decide in advance what happens when a retriever or tool is unavailable: answer with a stated gap, retry within a limit, or stop and report. Partial function should be designed, not discovered during an outage.
- Cost and step budgets. Cap model calls and tool invocations per request, and track model, memory, orchestration, and coordination costs as separate lines.
- Memory integrity, privacy, and retention. Define what persists between sessions, who can read it, how long it is kept, and how it is deleted. Persistent memory that is wrong or kept too long becomes a liability.
Diagnosing a bad answer in a RAG path
When a response is wrong, work backward through the path before rewriting the prompt. Most failures sit in one of four places.
- Check what was retrieved. Inspect the chunks returned for the query. If the relevant passage is missing, the cause is in ingestion, chunking, the embedding model, or search parameters, and prompt changes will not fix it.
- Check that the embedding model matches. Confirm that documents and queries were embedded with the same model and parameters. A mismatch shows up as weak retrieval across many queries rather than one bad query.
- Check the assembled prompt. Confirm that the retrieved content reached the model and was not truncated or pushed out by other context.
- Check the screen and the evaluation set. If the context was right and the answer still failed, find out whether the response screen let it through, and add this kind of question to the evaluation set so the regression is caught next time.
What the evidence establishes, and what it does not
- The AWS and Google Cloud material describes architecture patterns and vendor implementations. It does not establish comparative performance, a cost ranking, or a single best platform or orchestration pattern for every workload. Those claims need a benchmark scoped to your own workload.
- No cross-industry statistic is cited here, and no numerical benchmark is presented.
- The Google Cloud RAG reference evaluates responses for factual accuracy and relevance. That is one design choice, and nothing in the reference shows its measures transfer unchanged to other deployments.
- Dates matter for these sources. Google Cloud’s Generative AI with RAG index was reviewed September 22, 2025, the AlloyDB reference was last reviewed February 4, 2026, and the AWS Agentic AI Lens is dated June 10, 2026. Check the current revision of each before relying on specific service details.
The Bottom Line
Start with a static RAG path that has explicit ingestion, a pinned embedding model, and a standing evaluation loop. Add orchestration when the workflow must branch, and adopt agent-controlled retrieval or multi-agent delegation only when you can name the failure it fixes and measure that failure. Treat the controls above as launch requirements for any system that can act, not as later hardening.
Recommended Free Tools
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
