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AI Control Plane· Part 3

GuideFor: AI Engineers, ML Engineers, Platform Engineers, AI Systems Architects

Multi-Agent Pipeline Orchestration and Failure Propagation: Designing for Blast Radius

Retry logic tells an agent what to do when it fails. A pipeline halt protocol tells the entire fleet what to do. Most production systems only have one of these.

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#failure-propagation#pipeline-orchestration#langgraph#circuit-breaker#multi-agent#production-ai#control-plane

An inventory management agent hallucinated a SKU. The item didn't exist. The agent returned it as verified stock with a price, a quantity, and a warehouse location. That output passed schema validation - it was structurally correct JSON with all required fields populated. It failed downstream.

By the time the incident was caught, four systems had consumed the phantom SKU as truth. Step 6 generated a price quote. Step 9 updated inventory counts. Step 12 printed a shipping label. Step 15 sent a customer confirmation email. One hallucinated fact in step 1 triggered a four-system incident requiring manual rollback across ordering, fulfillment, inventory, and customer communications.

The root cause wasn't the hallucination. Hallucinations happen. The root cause was that the pipeline had no halt protocol - no designed mechanism to stop propagation the moment a structural fact produced by one agent was consumed by the next without verification. The pipeline had retry logic. It had circuit breakers on external API calls. What it didn't have was any answer to the question: "When Agent 1 fails, what exactly stops Agent 4 from acting on Agent 1's output?"

The Compound Reliability Problem

Before designing failure propagation, understand the math that makes it urgent.

A single agent completing a step with 99% reliability sounds strong. Chain 10 sequential steps at that rate and end-to-end pipeline reliability drops to 90.4%. Drop per-step reliability to 95% - still strong for any individual LLM task - and a 10-step pipeline delivers correct end-to-end results only 59.9% of the time. At 17 sequential steps and 95% per-step reliability, the pipeline is more likely to fail than succeed.

Carnegie Mellon benchmarks from 2025 showed even leading models completing only 30-35% of complex multi-step tasks autonomously. The reliability curve is not a model quality problem - it's a pipeline design problem. Better models shift the curve. They don't eliminate the compound effect.

This arithmetic has a direct architectural implication: the most important design decision in a multi-agent pipeline is not how to make each agent more reliable in isolation. It is how to prevent a single agent's failure from compounding into a pipeline-wide incident. The goal is minimizing Failure Blast Radius - the number of downstream agents and systems that act on an upstream failure before it is detected and contained.

Three Failure Modes That Propagate Differently

Not all failures propagate the same way. Getting this right determines what your halt logic needs to catch.

Hard failures are the easy case. An exception is raised, the node returns an error, the graph sees it. LangGraph's conditional edges can route to an error handler immediately. Blast radius is typically zero - the failure surfaces before downstream agents consume anything.

Soft failures are more dangerous. The agent completes successfully. Output is structurally valid. But the content is wrong - a hallucinated fact, a misclassified entity, a fabricated reference. These pass schema validation and flow directly into downstream agents as verified data. By the time the error surfaces, it has already been consumed by 2-5 downstream agents. This is what happened with the phantom SKU.

Cascading failures are the worst case. A soft failure propagates into Agent B, which incorporates the bad data into its own output. Agent B's output poisons Agent C. Agent C's output poisons Agent D. Each agent along the chain adds confidence to the bad fact because it was received from a "trusted" upstream source. Galileo AI's December 2025 research found that in simulated multi-agent systems, a single compromised agent poisoned 87% of downstream decision-making within four hours. The cascade looks like normal operation until you trace it back to the source - and without session replay from Part 1's unified observability layer, tracing it back takes days.

The Telemetry Surface Gap from Part 1 becomes lethal in cascading failures. Teams without Tier 3 behavioral metrics can't detect that Agent B's quality score dropped after consuming Agent A's output. They see successful completions at every node. They see the wrong end-user outcome hours later and have no signal pointing to where the cascade started.

What a Pipeline Halt Protocol Actually Is

Most engineers know what a circuit breaker does for a single agent - the Retry, Fallback, and Circuit Breaking layer from the Harness Engineering series covers this at the individual agent level. A pipeline halt protocol is different. It's the answer to: "When Agent 1 fails a semantic verification check, what instruction reaches Agent 4 before it acts?"

A pipeline halt protocol has four required components:

1. A halt signal - a typed, named value in pipeline state that all downstream agents check before executing. Not an exception. Not a log message. A state field that propagates through the graph.

2. A semantic verification gate - a check that runs on the output of each agent before it becomes input to the next, specifically for content correctness, not just structural validity. This is distinct from the schema validation in the Validation Layer - it checks whether the content makes sense in context, not just whether it has the right shape.

3. State preservation on halt - when the halt signal fires, the current pipeline state is checkpointed before any further execution. Recovery is possible from the point of failure, not from the beginning of the pipeline.

4. A propagation boundary - a defined set of which downstream nodes honor the halt signal and which can still execute safely (e.g., a logging node can run after a halt; a payment execution node cannot).

Wrong Way: Retry Logic Without Halt Logic

The naive approach handles failures per-agent without any pipeline-level signal. Each agent retries its own failures independently. No agent knows the state of any other agent's output quality.

code
# Wrong way: retry-only failure handling with no pipeline halt signal# Each agent retries independently. No halt propagation.from typing import TypedDict, Optional, Anyfrom langgraph.graph import END, StateGraphclass InvoiceState(TypedDict):    invoice_text: str    extracted_fields: dict[str, Any]    enriched_data: dict[str, Any]    payment_payload: dict[str, Any]    result: str    # No halt signal. No pipeline-level failure field.    retry_count: intdef extract_node(state: InvoiceState) -> InvoiceState:    """Agent 1: Extract fields from invoice text."""    try:        # LLM extraction - can hallucinate field values        fields = {"vendor": "Acme Corp", "amount": 4200.00, "currency": "USD"}        return {**state, "extracted_fields": fields, "retry_count": 0}    except Exception:        # Retries on hard failure. But soft failures (hallucinated values)        # return successfully and flow directly to enrich_node.        if state["retry_count"] < 3:            return {**state, "retry_count": state["retry_count"] + 1}        return {**state, "result": "extraction_failed"}def enrich_node(state: InvoiceState) -> InvoiceState:    """Agent 2: Enrich vendor data from CRM."""    # This node runs whether or not extract_node's output is correct.    # If extraction hallucinated a vendor name, this node queries the CRM    # for a vendor that doesn't exist, gets an empty result, and    # STILL passes enriched_data downstream as if the query succeeded.    fields = state["extracted_fields"]    enriched = {**fields, "vendor_id": "V-99999", "verified": True}    return {**state, "enriched_data": enriched}def payment_node(state: InvoiceState) -> InvoiceState:    """Agent 3: Construct payment payload."""    # This node acts on enriched_data that may be built on hallucinated    # extraction output. No signal from upstream tells it to stop.    data = state["enriched_data"]    payload = {"amount": data.get("amount"), "vendor_id": data.get("vendor_id")}    return {**state, "payment_payload": payload, "result": "ready"}# Problems:# 1. extract_node soft failures (hallucinated values) never trigger retry -#    they return as successes# 2. enrich_node and payment_node have no way to know extraction was wrong# 3. A hallucinated vendor name reaches the payment API before any human sees it# 4. Recovery requires restarting from scratch - no checkpoint before the failure

This architecture has a specific failure signature: the pipeline completes successfully, and the wrong payment is submitted. No exception was raised. No retry was triggered. Every node returned "result": "success" or its equivalent.

Right Way: Pipeline Halt Protocol with Semantic Verification

The correct architecture embeds a halt signal in the shared state and adds a semantic verification gate between each agent pair. When verification fails, the halt signal is set and propagates through the entire pipeline.
code
# Right way: Pipeline Halt Protocol with semantic verification gates# Halt signal propagates through state. Downstream agents check it before executing.# State is checkpointed on halt for recovery without full restart.from __future__ import annotationsimport hashlibfrom enum import Enumfrom typing import Any, Optional, TypedDictfrom langchain_openai import ChatOpenAIfrom langgraph.checkpoint.memory import MemorySaverfrom langgraph.graph import END, StateGraphfrom opentelemetry import metrics, traceclass HaltReason(Enum):    NONE = "none"    SEMANTIC_VERIFICATION_FAILED = "semantic_verification_failed"    POLICY_DENIED = "policy_denied"          # from Dual-Layer Gate (Part 2)    CIRCUIT_BREAKER_TRIPPED = "circuit_breaker_tripped"    MAX_RETRIES_EXCEEDED = "max_retries_exceeded"class PipelineState(TypedDict):    # --- Pipeline inputs ---    invoice_text: str    session_id: str    # --- Agent outputs ---    extracted_fields: dict[str, Any]    enriched_data: dict[str, Any]    payment_payload: dict[str, Any]    # --- Halt Protocol fields ---    halt: bool                        # the halt signal - all nodes check this first    halt_reason: str                  # HaltReason value - for audit trail    halt_agent: str                   # which agent set the halt    halt_checkpoint_id: Optional[str] # checkpoint ID taken at halt time    # --- Per-agent error tracking ---    retry_count: int    error_log: list[str]              # accumulates errors across all agents# --- Semantic verification gate ---class SemanticVerifier:    """    Runs between agent pairs to check content correctness, not just structure.    Structural validation lives in the Validation Layer (Harness Part 5).    This checks whether extracted facts are coherent and plausible in context.    Uses a small, fast model (not the same model that produced the output).    """    def __init__(self, verifier_model: str = "gpt-4o-mini") -> None:        self.llm = ChatOpenAI(model=verifier_model, temperature=0)    def verify_extraction(        self, original_text: str, extracted: dict[str, Any]    ) -> tuple[bool, str]:        """        Returns (is_valid, reason).        Checks whether extracted fields are supported by the source text.        Catches hallucinated values that pass schema validation.        Uses a different model than the extraction agent on purpose.        Asking the same model to verify its own output is unreliable -        it will often confirm its own mistakes. A smaller, independently        calibrated model catches hallucinations the extraction model won't        flag in its own output.        """        prompt = (            f"Source text:\n{original_text}\n\n"            f"Extracted fields:\n{extracted}\n\n"            "Do ALL extracted field values appear in or follow directly from "            "the source text? Respond with exactly one of:\n"            "VALID\n"            "INVALID: <one-sentence reason>"        )        response = self.llm.invoke(prompt)        content = response.content.strip()        is_valid = content.upper().startswith("VALID")        if not is_valid and ":" in content:            reason = content.split(":", 1)[1].strip()        elif not is_valid:            reason = content        else:            reason = "ok"        return is_valid, reason    def verify_enrichment(        self, extracted: dict[str, Any], enriched: dict[str, Any]    ) -> tuple[bool, str]:        """        Checks that enriched data is consistent with extracted data.        Catches cases where CRM lookup returned data for the wrong entity.        """        if not enriched.get("verified"):            return False, "Enrichment returned unverified vendor data"        if enriched.get("vendor_id", "").startswith("V-99999"):            return False, "Enrichment returned fallback vendor ID - lookup likely failed"        return True, "ok"# --- Pipeline nodes ---def make_halt_state(    state: PipelineState,    reason: HaltReason,    agent: str,    error: str,) -> PipelineState:    """    Sets the halt signal and records context.    Called by any node that detects a pipeline-stopping condition.    The checkpointer will snapshot this state automatically on the next step.    """    errors = list(state.get("error_log", []))    errors.append(f"[{agent}] {error}")    return {        **state,        "halt": True,        "halt_reason": reason.value,        "halt_agent": agent,        "error_log": errors,    }def extract_node(    state: PipelineState,    verifier: SemanticVerifier,    tracer: trace.Tracer,    meter: metrics.Meter,) -> PipelineState:    """Agent 1: Extract invoice fields. Applies semantic verification before passing output."""    with tracer.start_as_current_span("agent.extract") as span:        span.set_attribute("session.id", state["session_id"])        llm = ChatOpenAI(model="gpt-4o-2024-11-20", temperature=0)        response = llm.invoke(            f"Extract vendor, amount, currency, invoice_date as JSON "            f"from:\n{state['invoice_text']}"        )        # Structural validation: does the response parse as expected fields?        try:            import json            extracted = json.loads(response.content)            required = {"vendor", "amount", "currency", "invoice_date"}            if not required.issubset(extracted.keys()):                return make_halt_state(                    state,                    HaltReason.SEMANTIC_VERIFICATION_FAILED,                    "extract_node",                    f"Missing required fields: {required - set(extracted.keys())}",                )        except (json.JSONDecodeError, AttributeError) as exc:            return make_halt_state(                state,                HaltReason.SEMANTIC_VERIFICATION_FAILED,                "extract_node",                f"Output is not valid JSON: {exc}",            )        # Semantic verification: are the extracted values supported by the source?        is_valid, reason = verifier.verify_extraction(state["invoice_text"], extracted)        if not is_valid:            span.set_attribute("verification.failed", True)            span.set_attribute("verification.reason", reason)            return make_halt_state(                state,                HaltReason.SEMANTIC_VERIFICATION_FAILED,                "extract_node",                f"Semantic verification failed: {reason}",            )        span.set_attribute("verification.passed", True)        return {**state, "extracted_fields": extracted}def enrich_node(    state: PipelineState,    verifier: SemanticVerifier,) -> PipelineState:    """Agent 2: Enrich vendor data. Checks halt signal before executing."""    # All downstream nodes MUST check halt first.    # The halt signal is the propagation boundary.    if state.get("halt"):        return state  # do not execute - pipeline is halted    enriched = {        **state["extracted_fields"],        "vendor_id": "V-10042",        "verified": True,        "credit_limit": 50_000,    }    is_valid, reason = verifier.verify_enrichment(state["extracted_fields"], enriched)    if not is_valid:        return make_halt_state(            state,            HaltReason.SEMANTIC_VERIFICATION_FAILED,            "enrich_node",            f"Enrichment verification failed: {reason}",        )    return {**state, "enriched_data": enriched}def payment_node(state: PipelineState) -> PipelineState:    """Agent 3: Construct payment payload. Checks halt signal before executing."""    if state.get("halt"):        return state  # halt propagation boundary enforced    data = state["enriched_data"]    payload = {        "vendor_id": data["vendor_id"],        "amount": data["amount"],        "currency": data["currency"],        "reference": hashlib.sha256(            f"{data['vendor_id']}{data['amount']}{state['session_id']}".encode()        ).hexdigest()[:12],    }    return {**state, "payment_payload": payload}def audit_node(state: PipelineState) -> PipelineState:    """    Audit node: runs regardless of halt status.    Logs halt context for the audit trail established in Part 2's policy layer.    This node is deliberately NOT gated by the halt signal.    """    if state.get("halt"):        # Log the halt event - this feeds the fleet policy decision logs        print(            f"PIPELINE HALT | session={state['session_id']} "            f"reason={state['halt_reason']} "            f"agent={state['halt_agent']} "            f"errors={state['error_log']}"        )    return state# --- Routing ---def route_after_extract(state: PipelineState) -> str:    if state.get("halt"):        return "audit"    return "enrich"def route_after_enrich(state: PipelineState) -> str:    if state.get("halt"):        return "audit"    return "payment"# --- Graph assembly ---def build_invoice_pipeline(    verifier: SemanticVerifier,    tracer: trace.Tracer,    meter: metrics.Meter,) -> StateGraph:    """    Assembles the pipeline with halt protocol wired through routing functions.    Every conditional edge checks the halt signal before forwarding execution.    Closures bind dependencies so each node has the signature (state) -> state    required by LangGraph.    """    def _extract(state: PipelineState) -> PipelineState:        return extract_node(state, verifier=verifier, tracer=tracer, meter=meter)    def _enrich(state: PipelineState) -> PipelineState:        return enrich_node(state, verifier=verifier)    graph = StateGraph(PipelineState)    graph.add_node("extract", _extract)    graph.add_node("enrich", _enrich)    graph.add_node("payment", payment_node)    graph.add_node("audit", audit_node)    graph.set_entry_point("extract")    graph.add_conditional_edges(        "extract",        route_after_extract,        {"enrich": "enrich", "audit": "audit"},    )    graph.add_conditional_edges(        "enrich",        route_after_enrich,        {"payment": "payment", "audit": "audit"},    )    graph.add_edge("payment", "audit")    graph.add_edge("audit", END)    # Production checkpointer: state is preserved at every transition.    # On halt, the checkpoint at the halt node is recoverable for    # manual inspection and targeted re-run without full pipeline restart.    return graph.compile(checkpointer=MemorySaver())

Three properties make this correct:

  • Every downstream node checks halt first - the signal propagates through state, not through exceptions
  • Semantic verification runs between agents - soft failures that pass schema validation are still caught before they reach the next agent
  • audit_node is exempt from the halt gate - the audit trail runs regardless, feeding the fleet metrics from Part 1 and the policy decision logs from Part 2

The Semantic Verification Problem in Depth

The SemanticVerifier approach above works for simple fact-grounding. But it has limits worth being explicit about.

Using a second LLM to verify the first LLM's output only catches verification failures the verifier model is calibrated to catch. A slow, high-stakes pipeline can use a strong verifier. A high-throughput pipeline cannot afford two LLM calls per agent transition - the compound latency kills the system.

The right model for choosing your verification approach:

Pipeline TypeVerification ApproachRationale
Low-volume, high-stakes (finance, legal)LLM-as-judge with a strong modelAccuracy justifies the latency cost
High-volume, structured outputSchema + deterministic rules + spot samplingLLM verification on 5-10% of volume
Streaming pipelinesAsync verification post-executionCatch errors before final action, after fast path
Real-time / interactiveConfidence score threshold on extraction + rule checksNo added LLM call on the critical path

The key insight: semantic verification doesn't need to be LLM-based. For extraction agents, the most reliable checks are often deterministic: does the extracted amount appear as a number in the source document? Does the extracted vendor name appear as a string in the source document? These checks are fast, cheap, and catch the most common class of soft failure - hallucinated field values that bear no relationship to the source text.

Failure Propagation in Parallel Pipelines

Sequential pipelines are the easy case. Parallel pipelines add a new failure mode: a partial halt.

In a scatter-gather pattern - where a supervisor agent fans out to multiple worker agents and then collects results - a single worker failure creates an ambiguous state. Should the gather node wait? Proceed with partial results? Halt the entire pipeline?

The answer depends on whether the failed worker's output is required for downstream correctness. Define this explicitly in the pipeline design:

code
# Parallel pipeline with explicit failure tolerance configuration# Each worker declares its criticality to the gather node.from typing import Literalclass ParallelState(TypedDict):    query: str    session_id: str    # Worker outputs - Optional because workers may not complete    research_result: Optional[dict[str, Any]]    financial_result: Optional[dict[str, Any]]    legal_result: Optional[dict[str, Any]]    # Per-worker halt tracking    research_halt: bool    financial_halt: bool    legal_halt: bool    # Pipeline-level halt    halt: bool    halt_reason: str# Criticality config: which workers MUST succeed for the gather to proceedWORKER_CRITICALITY: dict[str, Literal["required", "optional"]] = {    "research": "optional",   # pipeline can proceed without this    "financial": "required",  # pipeline halts if this fails    "legal": "required",      # pipeline halts if this fails}def gather_node(state: ParallelState) -> ParallelState:    """    Gather node: checks per-worker halt flags against criticality config.    Only halts the pipeline if a REQUIRED worker failed.    """    for worker, criticality in WORKER_CRITICALITY.items():        worker_halt = state.get(f"{worker}_halt", False)        if worker_halt and criticality == "required":            return {                **state,                "halt": True,                "halt_reason": f"Required worker '{worker}' failed",            }    # Optional workers that failed: proceed with partial results,    # mark missing fields clearly so downstream agents don't hallucinate them    return {        **state,        "research_result": state.get("research_result") or {"status": "unavailable"},        "halt": False,    }

This pattern - explicit criticality declarations per worker, evaluated at the gather node - is what separates pipelines that degrade gracefully from pipelines that either silently drop required data or halt unnecessarily on non-critical failures.

Connecting Failure Propagation to the Control Plane

The pipeline halt protocol doesn't operate in isolation. It feeds directly into the control plane layers established in Parts 1 and 2.

When halt fires and audit_node runs, it should emit:

  1. A gen_ai.pipeline.halt metric event to the fleet telemetry plane (Part 1) with halt_reason, halt_agent, and session_id as attributes. This makes pipeline halts queryable in Grafana: "which agent is the most common halt source this week?"

  2. A policy decision log entry (Part 2) if the halt was triggered by HaltReason.POLICY_DENIED - connecting the gate layer's decision to the pipeline's operational outcome.

  3. A state checkpoint ID to a dead-letter queue for human review. Halted pipelines that affect money, compliance, or customer communications should not silently disappear. They should be visible, reviewable, and restartable.

The State Management layer from Harness Engineering covers how checkpoint IDs are generated and stored. The control plane's job is to make those checkpoint IDs visible at fleet scope - not just to the engineer who owns that specific agent.

Diagram: Pipeline Halt Protocol Flow

mermaid
flowchart TD
    IN["Invoice Text\n+ session_id"]

    subgraph Agent1["Agent 1: Extraction"]
        E1["LLM Extraction"]
        SV1{"Semantic\nVerification"}
    end

    subgraph Agent2["Agent 2: Enrichment"]
        HC2{"Halt\nCheck"}
        E2["CRM Lookup"]
        SV2{"Semantic\nVerification"}
    end

    subgraph Agent3["Agent 3: Payment"]
        HC3{"Halt\nCheck"}
        E3["Build Payload"]
    end

    AUDIT["Audit Node\nAlways runs\nEmits fleet metrics"]
    HALT["Halt State\nhalt=true\ncheckpoint preserved"]
    DLQ["Dead-Letter Queue\nHuman review\nRestart from checkpoint"]
    OUT["Payment Payload\nReady"]

    IN --> E1 --> SV1
    SV1 -->|"pass"| HC2
    SV1 -->|"fail"| HALT

    HC2 -->|"halt=false"| E2 --> SV2
    HC2 -->|"halt=true"| AUDIT
    SV2 -->|"pass"| HC3
    SV2 -->|"fail"| HALT

    HC3 -->|"halt=false"| E3 --> OUT --> AUDIT
    HC3 -->|"halt=true"| AUDIT

    HALT --> AUDIT --> DLQ

    style IN fill:#95A5A6,color:#fff
    style E1 fill:#4A90E2,color:#fff
    style SV1 fill:#7B68EE,color:#fff
    style HC2 fill:#7B68EE,color:#fff
    style E2 fill:#4A90E2,color:#fff
    style SV2 fill:#7B68EE,color:#fff
    style HC3 fill:#7B68EE,color:#fff
    style E3 fill:#4A90E2,color:#fff
    style AUDIT fill:#98D8C8,color:#333
    style HALT fill:#E74C3C,color:#fff
    style DLQ fill:#FFD93D,color:#333
    style OUT fill:#6BCF7F,color:#fff

The audit node at the bottom is the key structural element: it's the only node that runs in every path. Every other node has either a halt check or a verification gate. The audit node has neither - it runs unconditionally, which is what allows it to close the loop on fleet metrics regardless of whether the pipeline succeeded or halted.

Pipeline Orchestration Checklist

For every multi-agent pipeline:

  • halt field defined as a typed boolean in shared state - not buried in an ad-hoc error string
  • halt_reason and halt_agent fields defined for audit trail
  • Semantic verification gate between every agent pair where the downstream agent acts on upstream facts
  • Verification approach matched to pipeline type (LLM-as-judge / deterministic rules / confidence threshold)
  • Every downstream node checks halt as its first operation
  • At least one node explicitly exempt from the halt gate (audit, logging, metrics)
  • Conditional edges map halt paths to audit/dead-letter, not to END directly
  • Production checkpointer configured - state preserved at every transition
  • Dead-letter queue or human review queue receives halted session IDs with checkpoint reference
  • gen_ai.pipeline.halt metric emitted to fleet telemetry on every halt event

For parallel (scatter-gather) pipelines:

  • Worker criticality declared explicitly (required / optional) for every worker
  • Gather node evaluates criticality - does not treat all worker failures identically
  • Optional workers that fail return a marked-unavailable result rather than missing data
  • Downstream agents handle marked-unavailable fields without hallucinating substitutes

Cross-series connections:

  • Pipeline halts feed gen_ai.pipeline.halt counter to the Tier 2 harness metrics from Part 1
  • POLICY_DENIED halts generate a policy decision log entry per Part 2's OPA integration
  • Checkpoint IDs at halt time are surfaced to the fleet dashboard, not buried in per-agent logs

What Comes Next

The pipeline halt protocol governs what happens when an agent fails during execution. Part 4 of this series covers Agent Versioning and Deployment Strategies - what happens when you need to update an agent that is currently running as part of an active pipeline. Rolling deployments, blue-green agent swaps, and the version consistency problem: how do you ensure that Agent 1 v1.3 and Agent 2 v2.0 are tested together before the fleet consumes the combination?

References

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