Agentic Search Architecture: How Production-Ready Search Agents Work
Key Takeaways
- A production agentic search architecture controls an entire research trajectory, not one search call. It converts a goal into evidence requirements, discovers and reads sources, evaluates what is missing, changes its plan when needed, and stops with a validated result.
- Production readiness depends on explicit controls around the adaptive loop. Typed tool contracts, persistent state, failure-specific retries, observability, evidence validation, and time, cost, token, and tool-call budgets keep flexible search behavior within predictable operating boundaries.
- Tool routing should follow the information gap. Search is appropriate when the source is unknown, direct page retrieval when the URL is known, browser infrastructure when your code must control an interactive session, and a web agent when the task requires goal-driven decisions across multiple pages. The route can start at any level and should escalate only when the current method cannot obtain the required evidence.
- TinyFish’s production story is the progression between web primitives. Search, Fetch, Browser, and Web Agent let a team match web capability to task complexity instead of forcing every retrieval need through the same execution method. The application still owns its evidence rules, budgets, validation, and user experience.
A search agent can work in a demo and still fail under production workloads. One query may miss the right source, a selected page may return incomplete evidence, a retry may repeat the same failure, or several individually acceptable steps may push the complete task beyond its latency and cost limits. A production-ready agentic search architecture must therefore control the whole path from the original goal to a source-backed, validated result. This guide explains that architecture, including tool routing, retries, failure handling, observability, cost and latency controls, and escalation between web capabilities.
Agentic Search Architecture at a Glance
Agentic search architecture is the system that lets an agent plan, execute, evaluate, and adapt web research while preserving the goal and operating constraints across multiple steps. The model supplies reasoning, but production capability comes from the surrounding control loop: state, typed tools, evidence handling, runtime policies, and validation.
State, observability, security, and runtime budgets surround the complete loop. The evaluator can send the workflow back to planning, route it to another retrieval or access method, stop at a budget boundary, or forward a sufficiently supported result to validation.
This foundation also separates the broader architecture from individual tools. Search, page extraction, browser infrastructure, and web agents are capabilities the router can invoke; none of them independently supplies the full production control loop.
The architecture must cover the full workflow instead of only search and page access:
Table 1: What stages should a full production-ready agentic search architecture have?
| Architecture stage | What the system does | Why production needs it | Failure if it is missing |
|---|---|---|---|
| Planning | Converts the goal into required facts, dependencies, source requirements, and success criteria | Gives the workflow a testable definition of completion | The agent searches extensively but omits a field the application needs |
| Search | Generates and revises queries, diversifies sources, and applies freshness or domain constraints | Finds current evidence even when the correct source or terminology is unknown | One weak query determines the entire answer |
| Evidence acquisition | Selects promising results, retrieves the underlying pages, and preserves source metadata | Turns candidate URLs into usable, attributable evidence | Unverified snippets, blocked pages, or partial content are treated as sufficient proof |
| Evaluation | Checks coverage, authority, freshness, contradictions, and claim-to-source support | Determines whether the evidence actually satisfies the task | The system stops on a plausible answer instead of a supported one |
| Iteration | Reformulates queries, changes sources, or retries recoverable failures | Lets the workflow respond when the first path is incomplete | The agent either stops too early or repeats the same unsuccessful call |
| Escalation | Changes the access method when the evidence cannot be obtained through the current one | Reaches rendered, interactive, stateful, or multi-page information without using that machinery for every task | Simple tasks are over-engineered, while difficult sources remain inaccessible |
| Production controls | Enforces state, security, retries, timeouts, budgets, observability, and human-review rules | Keeps adaptive behavior repeatable, diagnosable, and bounded | Failures become silent, runs become unpredictable, and operating cost compounds |
| Result contract | Returns structured fields, sources, validation status, unresolved questions, and termination reason | Lets downstream software distinguish success, partial completion, and failure | A syntactically valid response reaches the application without sufficient evidence |
Why Production Agentic Search Needs Controlled Escalation
Controlled escalation means changing the query, source, or web-access method in response to a diagnosed evidence gap. It does not mean every task must proceed through a fixed sequence of increasingly complex tools.
A February 2026 architecture preprint describes production-grade agents as systems that separate reasoning from execution and place typed tool interfaces, state machines, retry and backoff logic, circuit breakers, tracing, policy enforcement, and cost and rate limits around the agent loop. Its enterprise hardening checklist marks end-to-end tracing and budgeted autonomy- caps on tokens, time, cost, and tool calls- as required controls. The paper is an architectural synthesis rather than a controlled performance study, but its framework is useful for defining what production readiness requires.
Table 2: Escalation paths to the different types of production bottlenecks
| Current bottleneck | Appropriate change | Why it is not automatically a heavier-tool problem |
|---|---|---|
| The query returns weak or repetitive results | Reformulate the query or change source constraints | The access method works; the information request is underspecified |
| The result looks relevant, but the page content is incomplete | Retrieve another source or change the extraction approach | More browser autonomy will not repair a poor source-selection decision |
| The page depends on rendering, pagination, filters, or session state | Use browser-backed access | The evidence exists, but the current retrieval method cannot reach it |
| The task requires decisions across several pages | Use a goal-driven web workflow | The system needs persistent state and adaptive execution across a longer path |
| Credible sources conflict | Search for stronger or independent evidence | The problem is evidence quality, not page access |
| The run reaches its operating boundary | Stop, return partial status, or request review | Escalation must not override cost, latency, security, or approval limits |
It is worth noting that latency is determined by various factors. End-to-end time can increase because the agent generates too many sub-queries, receives low-value results, retrieves unusable content, repeats model evaluation, waits in a queue, retries a transient failure, or performs a long sequence of page actions. Thus, a production router weighs expected evidence value, reliability, and remaining budget, not simply “light” versus “heavy” tools.
Each layer also has its own tool category and evaluation criteria:
- web search APIs for source discovery
- web data extraction tools for turning selected pages into usable content
- browser infrastructure for direct control of rendered, interactive, or stateful pages
- web agents for goal-driven, multi-page workflows
How a Production Agentic Search Workflow Actually Runs
The workflow contains 11 actions, but they form seven connected phases. Each phase closes a production risk and passes explicit state to the next one.
Table 3: 7 phases of an agentic search run
| Steps | Phase | Decision the architecture must make |
|---|---|---|
| 1–2 | Define the task | What evidence and output would count as completion? |
| 3 | Plan the retrieval work | Which information needs are independent, and which depend on earlier findings? |
| 4 | Generate queries and choose the initial route | How should the system begin looking for each piece of evidence? |
| 5–7 | Discover, select, and retrieve sources | Which results are worth reading, and did their pages return usable evidence? |
| 8 | Evaluate the evidence | Is the evidence sufficient, incomplete, conflicting, or inaccessible? |
| 9–10 | Recover or escalate | Should the system revise the query, retry a failure, change sources, or change access methods? |
| 11 | Stop and validate | Has the workflow met its evidence contract or reached a defined boundary? |
1. Define the Goal and Evidence Contract
The workflow begins by preserving the original goal together with the application’s constraints. It then translates that goal into required facts or fields, acceptable source types, freshness requirements, and the intended output format.
In this guide, evidence contract is a working term for the task-specific requirements the architecture will test: the facts or fields to collect, acceptable sources and freshness, required provenance, output format, and the conditions for completion, partial completion, failure, or human review. It is not presented as a standardized industry architecture term. This working contract gives the evaluator something concrete to test; without it, the agent may return a plausible response without collecting the information the application actually needs.
For example, “Find the company’s latest product announcement” can become six requirements: the announcement, publication date, affected product, first-party source, independent confirmation, and a defined freshness window. The trace should store that checklist before retrieval begins.
2. Decompose the Information Needs and Order the Work
The planner breaks the evidence contract into retrieval needs and identifies the dependencies between them. Independent questions can run in parallel, while dependent questions must wait for an entity, date, location, or claim found during an earlier step.
This prevents indiscriminate parallelization. Searching for an unnamed executive’s previous employer, for instance, cannot begin until the first retrieval step establishes the person’s identity. The plan should preserve resolved facts, open questions, and the original constraints so later queries do not drift away from the task.
ChainRAG identifies a related “lost-in-retrieval” failure in multi-hop question answering: key entities can disappear during sub-question decomposition, breaking the later retrieval and reasoning chain. Its progressive method rewrites each dependent sub-question using information recovered in the preceding step. For production architecture, the useful principle is that decomposition creates a dependency graph, not a collection of context-free searches.
3. Generate the First Queries and Choose the Initial Route
The system generates a query or action for each ready retrieval need; the initial route follows what the system knows about the evidence:
Table 4: Example of tool routing condition in an agentic search infrastructure.
| Starting condition | General capability to use first | Production rationale |
|---|---|---|
| The source location is unknown | Web search | Discover candidate sources before paying to inspect or operate them |
| The source URL is known and publicly readable | Direct page retrieval or extraction | Avoid a search round that cannot add discovery value |
| The application must control page actions or session state | Browser infrastructure | Keep deterministic navigation and execution logic in the application |
| The task requires adaptive decisions across several webpages | Goal-driven web agent | Preserve a goal and state while the execution route changes across pages |
The routing decision should record the chosen capability, the reason it fits the evidence need, and the expected result. That makes later escalation explainable: the system can show what assumption failed instead of merely reporting that another tool ran.
Search-R1 provides evidence for the iterative planning behind this phase. Across seven question-answering datasets, its learned multi-turn search achieved average relative improvements of 41% for Qwen2.5-7B and 20% for Qwen2.5-3B over retrieval-augmented generation baselines under the same experimental setting. The result does not mean every task needs several searches; it shows why the architecture must be able to revise a query when the first retrieval round is insufficient.
4. Discover, Select, and Retrieve Evidence
For claims that require a site’s full content, treat Search results as candidates until the underlying content is inspected. The workflow first discovers relevant sources, then evaluates which results are likely to contain the required detail based on relevance, authority, freshness, independence, and accessibility.
Only the strongest candidates should move to page retrieval. This reduces unnecessary calls and prevents duplicate or low-value material from consuming the context budget. The architecture should record why each source was selected or rejected rather than treating rank position as a sufficient quality signal.
Page retrieval then tests whether the selected URL actually provides usable content. The result should retain the URL, title, date, retrieval status, relevant content, and any indication that the page was empty, truncated, blocked, or incomplete. A promising snippet cannot substitute for evidence that never arrived from the underlying page.
This selection layer protects answer quality as well as efficiency. An ACL 2024 study found that inappropriate retrieved passages can impair a model’s ability to generate high-quality responses and grouped real-world retrieval noise into several distinct types. The architectural implication is that successful retrieval must be judged by usable signal and support for the task.
5. Decide Whether the Evidence Is Sufficient
The evaluator compares the evidence workspace with the contract defined at the beginning. It checks whether required fields are covered, sources are appropriate and current enough, important claims remain traceable, and credible disagreements have been resolved or represented honestly.
That assessment produces one of five states:
- Sufficient: required evidence is present and can move to output validation
- Incomplete: a material fact or source is still missing
- Conflicting: credible sources disagree in a way that could change the answer
- Inaccessible: the likely evidence cannot be reached through the current method
- Budget exhausted: further work would exceed a time, cost, token, retry, or policy boundary
These states drive the next action. The architecture should not leave continuation or stopping to an unrecorded model judgment because operators need to understand why another attempt was, or was not, made.
6. Revise, Retry, or Escalate According to the Failure
Retries should be failure-specific and bounded. Repeating the same action only makes sense for a likely transient error; information, extraction, and page-state failures require a changed strategy.
Table 5: Failure handling guardrails for agentic search architecture.
| Failure class | Productive recovery | What to record | Safe terminal outcome |
|---|---|---|---|
| Weak or ambiguous results | Reformulate the query with missing entities or constraints | Previous query, diagnosis, and revised intent | Unresolved information need |
| Duplicate or low-authority sources | Change source type, domain, or corroboration requirement | Accepted and rejected sources | Insufficient corroboration |
| Empty, truncated, or blocked page content | Try another strong source or change access method | Retrieval error and content status | Evidence inaccessible |
| Transient timeout or service error | Retry with a capped count and backoff | Error category, attempt count, and delay | Explicit tool failure |
| Rate or concurrency limit | Respect retry timing or reduce submission rate | Queue depth, rate-limit response, and remaining deadline | Deferred or incomplete |
| Browser action or unexpected page state | Reinspect state, replan, or restart from a safe checkpoint | Last confirmed state and consequential actions already taken | Stop before duplicating an action |
| Invalid or unsupported output | Retrieve only the missing evidence and rerun validation | Field-level validation errors | Partial result or human review |
Repeating the same call without changing the failure condition is not a recovery strategy. Every retry should preserve the original goal, previous attempts, evidence already collected, and any side effects so the system does not erase progress or repeat a consequential action.
Retries and escalation must also remain inside the task budget. When attempts are exhausted, the system should return an explicit unresolved need, access limitation, tool failure, partial result, or review request rather than converting failure into a fluent answer.
7. Stop and Validate the Result
The workflow stops when the evidence contract is satisfied or when a time, cost, retry, access, security, or approval boundary prevents further work. These conditions should produce distinct terminal states such as completed, incomplete, failed, cancelled, or human review required.
Before release, the output validator checks required fields, data types, source coverage, freshness, allowed domains, unresolved questions, and task-specific completion rules. Structured output makes the response predictable, but only evidence validation makes it trustworthy.
The final result should expose its status, supporting sources, unresolved questions, validation outcome, termination reason, and error code where relevant. That lets downstream software decide whether to use the result, retry later, or send it for review.
Observability Makes the Search Trajectory Debuggable
Agentic search needs step-level observability because the final answer alone cannot show whether the failure began in planning, search, source selection, extraction, evaluation, or page execution. The architecture must trace both execution state and task quality: a workflow can finish technically while still returning incomplete or unsupported evidence.
Table 6: What observability in an agentic search architecture should answer
| Stage | Minimum observable data | Question it lets developers answer |
|---|---|---|
| Goal and plan | Original goal, decomposition, dependencies, policies, and output requirements | Did the system understand what completion meant? |
| Tool routing | Candidate tools, selected route, and reason for escalation | Did the architecture choose a suitable way to reach the evidence? |
| Search | Queries, filters, result metadata, duration, and errors | Did poor discovery cause the downstream gap? |
| Source selection | Accepted and rejected results with reasons | Did the evaluator choose weak or duplicate evidence? |
| Page retrieval | URL, status, content completeness, timestamps, and extraction errors | Was the necessary evidence actually returned? |
| Iteration and retries | Failure classification, revised action, attempt count, and backoff | Did each retry change the likelihood of success? |
| Browser or web-agent execution | Actions, page state, checkpoints, screenshots or snapshots where appropriate | Where did an interactive workflow diverge from the intended path? |
| Evaluation and output | Claim-to-source coverage, validation errors, status, and termination reason | Why did the workflow stop, and can the application trust the result? |
| Runtime | Per-step and total latency, tokens, calls, concurrency, and cost | Which part of the trajectory breached the operating budget? |
The production architecture paper identifies end-to-end structured tracing, standardized metadata, audit logs, and model, prompt, and tool versions as required evidence artifacts for reproducible agent behavior. That turns observability from a dashboard feature into part of the execution contract.
Cost and Latency Controls Bound the Whole Workflow
Production teams should budget the complete task, not only the search request or browser session. A useful model is:
Total task latency = planning + search rounds + page retrieval + model evaluation + retries + interactive execution + queueing
Total task cost = model tokens + tool calls + page or browser usage + retries + infrastructure overhead
The two are connected but not interchangeable. More parallel retrieval may reduce elapsed time while increasing calls and tokens; a slower, higher-quality retrieval can sometimes reduce total latency if it prevents additional reasoning and retries.
Table 7: Controls to Define Before the Run
| Budget | What to cap or measure | What happens at the boundary |
|---|---|---|
| Time | Total deadline, per-step timeout, queue time, and remaining time before escalation | Stop, return partial status, or switch to a predefined fallback |
| Search and retrieval | Query rounds, results inspected, pages retrieved, and duplicate rate | Prioritize unresolved high-value evidence and stop low-yield expansion |
| Reasoning and context | Model calls, input/output tokens, and evidence workspace size | Compress evidence, use a smaller decision model, or stop |
| Retries | Attempts by failure class and cumulative delay | Open a circuit breaker or require review |
| Interactive execution | Browser actions, web-agent steps, session duration, and consequential actions | Stop at a checkpoint without repeating side effects |
| Concurrency | Parallel queries, page requests, and active sessions | Apply backpressure instead of allowing queue growth |
| Cost | Estimated and actual task cost by stage | Return the best validated partial result within policy |
SearchAgent-X demonstrates why latency has to be evaluated end to end. Its authors found that both exact retrieval overhead and overly approximate retrieval could reduce efficiency: coarse results required more reasoning, while retrieval stalls disrupted inference scheduling. Their framework achieved up to 3.4× higher throughput and 5× lower latency than the tested baselines, but those numbers describe their experimental system rather than a universal multiplier. The architectural lesson is that retrieval quality, scheduling, and reasoning steps jointly determine runtime.
What a Production-Ready Agentic Search Architecture Must Be Able to Do
A production system should demonstrate these capabilities under representative workloads, not merely implement the controls on paper. TinyFish’s customer stories show the type of result each capability supports; they do not disclose or prove the customer’s internal architecture.
Table 8: 5 capabilities a production-ready agentic search architecture should have.
| Required production capability | Real-world evidence from TinyFish’s customer cases. | What developers should test | Consequence if the capability fails |
|---|---|---|---|
| Repeat multi-step web work at sustained volume | DoorDash reports more than 1 million quarterly web interactions across authenticated systems, dynamic pages, and global workflows | Completion rate by route, per-run overhead, retry rate, concurrency behavior, and failure visibility | Small inefficiencies and silent partial failures compound across the workload |
| Reach fragmented sources that do not expose a standard integration | Google Hotels reports reaching 40,000+ properties by navigating booking flows, checking date-specific availability, extracting prices, and returning structured results | Coverage across the long tail, interactive access success, field consistency, and freshness | The platform misses supply or sends inconsistent availability and pricing downstream |
| Verify changing information and surface discrepancies | ClassPass reports automated checks across 835 venues, including sign-in, live schedule verification, availability checks, and discrepancy flags | Authentication, session reuse, evidence freshness, match-versus-discrepancy logic, and review handoff | A missing or misread schedule is treated as a confirmed update instead of an exception |
| Stay inside an end-user latency requirement | The Zebra reports quote turnaround under two minutes and workflow execution in seconds across multiple quote sources | End-to-end p50/p95 latency, slow-path frequency, retry contribution, and timeout behavior | The web workflow delays the customer experience even if individual calls remain within their own limits |
| Operate as a dependable component inside a larger agent system | TestSprite uses TinyFish for browser interaction within an autonomous testing pipeline serving approximately 100,000 users | Component boundaries, error categories, run artifacts, replayability, and ownership of orchestration | The team cannot isolate whether failure came from its planner, the website, or browser execution |
Production-Readiness Checklist
Use this checklist against representative tasks before calling an agentic search architecture production-ready:
[ ] The task has explicit required fields, evidence rules, and completion states
[ ] The planner distinguishes independent from dependent retrieval needs
[ ] Initial tool routing is based on what the system knows about the evidence
[ ] Search results are evaluated before pages are retrieved
[ ] Page content is checked for completeness and kept with provenance
[ ] Evidence sufficiency and contradiction rules are defined before the run
[ ] Query revision is separated from access escalation
[ ] Retries are classified, bounded, and safe around consequential actions
[ ] Every route returns explicit success, partial, failed, cancelled, or review-required status
[ ] Traces preserve queries, tool decisions, evidence, errors, route changes, and termination reason
[ ] Time, tokens, calls, steps, concurrency, retries, and total cost have task-level budgets
[ ] Structured output is validated against both its schema and supporting evidence
[ ] Representative tasks are tested across repeated runs, not only successful demonstrations
[ ] Security and human-approval boundaries are enforced before authenticated or consequential work
How TinyFish Fits the Controlled-Escalation Production Architecture
TinyFish provides the web-execution layer for production Agentic Search, from source discovery and page retrieval to browser control and goal-driven multi-page execution. Its value is not simply that it offers Search: its four web primitives support different information-gathering conditions, allowing the application to use the capability suited to the current evidence gap and move to another only when the task demands it. The application still owns its broader planner, evidence policy and evaluator, total-task budgets, output validation, and user experience.
Table 9: TinyFish: production-ready Agentic Search layer, providing structured output, and multi-step web research
| Information-gathering condition | TinyFish primitive | What it contributes | What the application still owns |
|---|---|---|---|
| The agent does not know where the evidence is | Search | Returns ranked titles, snippets, URLs, and search controls for discovery | Query planning, source policy, evidence selection, and stopping |
| The agent knows which URL contains the likely evidence | Fetch | Renders when needed and returns extracted Markdown, HTML, or JSON from the selected page | Evidence requirements, completeness checks, and claim validation |
| The application needs direct control over an interactive or stateful page | Browser | Creates a managed browser session that the application controls through Playwright or Chrome DevTools Protocol | Every page action, retry, checkpoint, and completion decision |
| The task requires goal-driven decisions across a multi-page workflow | Web Agent | Powered by Mako, TinyFish’s web-native model; accepts a goal and URL, decides browser actions, maintains run state, and can return typed JSON through output_schema | Business rules, evidence thresholds, total-task budgets, validation, and user experience |
A workflow can begin with Fetch when the URL is known, Browser when deterministic code already owns the interaction, or Web Agent when the task is inherently multi-step. It can also remain on Search or Fetch when retrieval already satisfies the evidence contract.
TinyFish Observability Supports the Web-Execution Boundary
TinyFish Web Agent exposes run states: PENDING, RUNNING, COMPLETED, FAILED, and CANCELLED, along with timestamps, step counts, results, structured error categories, and step records. Runs can also expose live progress, screenshots or HTML snapshots when captured, a streaming view, and terminal-state webhooks. These features help a team inspect the web-execution portion of the trajectory; the surrounding application must still trace its own planning, source selection, evidence evaluation, and final validation. (For details, read: TinyFish run lifecycle, webhooks)
Web Agent run data includes timestamps and step counts that help teams measure the execution path. Regardless of provider, the application should maintain its own task-level limits for time, tokens, retries, concurrency, and total cost.
Search and Fetch Evidence Should Be Evaluated Together
OpenBenchmarks holds the model, task set, budgets, and runner fixed while changing the search-and-fetch provider. On its September 2026 board, configurations ranged from 48.3% to 83.0% grounded task completion. TinyFish completed 79.0% ± 2.0 of 100 coding tasks with 12,844 median task tokens, a 24-second median task time, and a $0.050 median task cost; Exa Deep led completion at 83.0% ± 1.0. The benchmark is limited to enterprise-documentation coding tasks, but it shows why retrieval quality, tokens, latency, and total task cost must be evaluated together rather than as isolated API metrics.
The result reinforces that source discovery and page extraction influence the rest of the agent’s trajectory, while interactive or multi-page work may require Browser or Web Agent. The architecture chooses among those primitives based on the task instead of treating one surface as the answer to every web problem.
Build the smallest reliable control loop for your agentic search architecture.
Start with one representative production task. Define its evidence contract, initial route, escalation triggers, retry policy, trace fields, operating budget, output schema, and terminal states before testing the complete trajectory across repeated runs.
Then add the next web capability only when the trace shows that the current route cannot reliably obtain the evidence. This keeps the architecture proportional to the workload while preserving a path from straightforward discovery and extraction to controlled browser sessions and goal-driven multi-page execution.
Try the representative task in the TinyFish Playground, then review the run state, output, evidence, latency, and failure behavior before expanding the workload.
Frequently Asked Questions
How Does Agentic AI Search for Information?
Agentic AI searches for information by turning a goal into one or more evidence needs, generating queries, evaluating results, and retrieving the sources required by its evidence policy. It then checks whether the collected evidence is sufficient. If information is missing, conflicting, or inaccessible, it can revise the query, choose another source, retry a recoverable failure, or change access methods before stopping with a validated result or an explicit limitation.
What Should an Agentic Search Evidence Workspace Store?
An evidence workspace should store the original question, required fields, resolved and unresolved facts, source URLs, retrieval timestamps, short supporting passages, source type, contradictions, rejected-source reasons, and claim-to-source relationships. Keep this state separate from an ever-growing conversation transcript so the system can retrieve only the evidence needed for the next decision.
How Should Conflicting Sources Appear in Structured Output?
Do not collapse credible disagreement into one confident value. Return the competing claims with their sources and dates, identify which source is authoritative for the field when a rule exists, and set the field or result to uncertain when the architecture cannot resolve the conflict within its evidence or budget policy.
When Should Agentic Search Require Human Review?
Require review when evidence remains materially incomplete or contradictory, a consequential action cannot be safely repeated, the source or credential policy blocks completion, or the result would trigger a high-impact business decision. Human review should be an explicit terminal status with the evidence and unresolved decision attached.
How Does Caching Fit Into Agentic Search Architecture?
Caching is appropriate when the task can tolerate information as old as the cache policy allows. Set freshness requirements per task or field, store retrieval timestamps, and force live retrieval for rapidly changing information such as availability, prices, or breaking events.
Can Agentic Search Combine Public Web and Private Company Data?
Yes. A retrieval router can use public search, internal indexes, databases, and approved business tools, then place evidence in a shared workspace with source labels and access controls. In an agentic RAG architecture, internal indexes can supply private knowledge while live web retrieval supplies current external evidence, with both evaluated against the same task requirements. The evaluator must preserve the boundary between public and private evidence so the output does not expose restricted information.
How Should Teams Test Agentic Search Against Website Changes?
Maintain a representative task suite across the websites and flows that matter, including known edge cases and failure states. Rerun it when prompts, models, tools, schemas, or target sites change, then compare grounded completion, route choice, step count, latency, cost, and trace differences against the previous version.
What Security Controls Are Needed for Authenticated Web Research?
Keep credential values outside the model context, scope access to the required domains and actions, isolate sessions, record consequential steps, and require approval for sensitive changes. The result should state what was verified without returning passwords, tokens, cookies, or other session secrets.
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