The Tool Desk
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Production AI fails in ways a controlled demo cannot reveal: an agent may access stale or sensitive data, call a tool with excessive authority, loop indefinitely, or make an incorrect change without returning an error. The practical response is not simply a larger model. Teams need explicit permission boundaries, verification for every consequential action, telemetry that reconstructs what happened, and a named human owner for the outcome.
What the production problem actually is
An AI workflow that passes a test set runs under very different conditions once real users, distributed permissions, operational limits and incident pressure enter the picture. Production readiness therefore means proving four things for each task: what the system accessed, which actions it was allowed to take, how its output was verified and who remains accountable when it fails.
Those questions frame InfoQ’s free 60-minute online panel, AI in Production: What Breaks, What Works, and Who Approves It?, announced for October 14, 2026. The participants bring complementary experience: Hien Luu works on ML platforms, retrieval-augmented generation (RAG), agents and reliability; Katharine Jarmul focuses on privacy and security; Zichuan Xiong works on agentic systems and coding-agent harnesses; Premanand Chandrasekaran covers permissions, review gates and governance; and Lan Chu builds production data and ML pipelines and RAG systems using more than 10,000 documents.
The agenda concentrates on agent autonomy, verification of AI-generated changes, sensitive-data exposure, production RAG, platform architecture and the organizational case for stronger controls before an incident forces the issue.
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Why silent failures are more dangerous than visible errors
Reported failure rates are high, but narrowly qualified
A 2026 Linux Foundation session description reports multi-agent failure rates of 41% to 86% across seven frameworks. It attributes the Multi-Agent Systems Failure Taxonomy (MAST) to work from UC Berkeley and NeurIPS 2025 and says the taxonomy groups failures across more than 1,600 execution traces. These figures are claims in the session description, not a universal production benchmark; teams should check the underlying MAST publication before treating them as independent statistics.
Two incidents show how the damage stays hidden
The same description gives a LangGraph customer-service example in which one unvalidated tool call silently corrupted hundreds of Zendesk support tickets. It also describes a Kubernetes-hosted presales assistant that entered an indefinite loop on an out-of-distribution input and consumed compute before anyone detected it. In both cases, the system did not present a conventional application error. A successful tool response or a running process was mistaken for a successful task.
Absence of an error is not evidence of correctness
Silent failure controls must check the result, not only the transport. A useful audit record answers which tools were called, with which arguments, in what order, using which identity, against which data and with what verification result. Without that record, operators cannot distinguish a correct answer from an unauthorized change, a stale retrieval or an incomplete workflow.
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Harness engineering turns failure modes into controls
Harness engineering is the practice of surrounding an agent with explicit contracts, orchestration rules and observability so that its behavior is constrained and inspectable. The Linux Foundation session describes a stack built from four cooperating layers:
MCP-native tool contracts
Tool contracts define the operations an agent can invoke and constrain their inputs and outputs. A contract should express allowed parameters, validation rules, side effects and the identity under which the call executes. High-impact operations can be exposed only through a review or approval path rather than as ordinary tools.
LangGraph orchestration harnesses
An orchestration harness makes workflow state and transitions visible. It can limit retries, detect loops, enforce timeouts and stop execution when a prerequisite check fails. These controls contain a runaway agent before it consumes unbounded compute or repeats a destructive action.
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Pydantic schema contracts
Schema validation prevents free-form model text from becoming an unchecked command. Structured inputs and outputs make it possible to reject missing fields, invalid values and unexpected tool results before the next workflow step runs. Schemas improve reliability, but they do not prove that a validly shaped action is appropriate; policy and business checks are still required.
Opik telemetry
Telemetry supplies the evidence needed for task verification and incident diagnosis: prompts and model outputs where policy permits, retrieved documents, tool arguments, state transitions, latency, retries, approvals and final outcomes. Retention and access to these traces must themselves follow privacy and security requirements.
These layers are complementary. A schema can reject malformed data, while orchestration stops a loop and telemetry explains what happened. None substitutes for an owner who can approve or reject a consequential action.
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Who should approve an agent’s actions?
Governance starts by classifying actions according to impact rather than treating every model response alike. The panel agenda surfaces five decisions that should be explicit in a production design:
- Autonomy boundary: specify which actions are read-only, reversible or low impact and may run automatically, and which actions require a human gate.
- Change verification: require generated code, configuration and database changes to pass tests, static checks, policy checks and review before CI or deployment.
- Data authority: define which sensitive or regulated data the agent may retrieve, for which purpose, and under whose identity. Model threats before granting access.
- Reconstruction: retain an audit trail showing data access, tool calls, sequence, approvals and the authority used for each step.
- Accountability: name the person or team that owns the decision and the recovery plan when an agent causes harm or silently changes records.
A human approval button is not sufficient if the reviewer cannot see the proposed change, evidence behind it, affected records and rollback option. Approval must be informed, attributable and logged.
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How production control patterns trade off
There is no single autonomy setting that fits every workload. The following framework compares common operating patterns against the dimensions raised by the panel and harness-engineering session.
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| Control pattern | Autonomy and approval | Verification and auditability | Data scope and containment | Operating trade-off |
|---|---|---|---|---|
| Prototype demonstration | Broad, implicit autonomy; approval is usually outside the workflow | Scenario checks with limited execution evidence | Often uses a convenient dataset and simplified credentials | Fast to build, but weak evidence and weak recovery controls |
| Guarded single agent | Read actions can be automatic; writes pass defined gates | Tool validation, output checks and traceable decisions | Least-privilege credentials and an explicit retrieval boundary | More engineering and latency in exchange for clearer failure containment |
| Multi-agent production harness | Each agent has a narrow role; cross-agent or high-impact actions require policy approval | State-machine checks, schemas, telemetry, task-level verification and replayable traces | Separate identities, scoped tools and limits on shared context | Highest control and operating complexity; useful when workflows and consequences are substantial |
RAG reliability extends beyond a successful demo
Retrieval-augmented generation can answer a curated question while failing in production because documents change, permissions differ, queries are ambiguous or the retriever returns plausible but irrelevant passages. Lan Chu’s experience with systems built on more than 10,000 documents illustrates the operational scale: teams must monitor ingestion, freshness, indexing, retrieval quality and access controls, not only the final prose answer.
For each retrieved answer, production checks should establish that the user was authorized to see the source, the evidence is current enough for the task, citations or source references support the claim where required, and the system can decline when retrieval confidence is inadequate. Sensitive-data threat modeling belongs before deployment, not after a leak.
A verification path for generated changes and tool calls
- Declare the action: represent the requested task and intended side effect in a structured format.
- Check authority: resolve the user, service identity, data classification and permitted tools before execution.
- Validate the plan: reject unsupported parameters, excessive scope, missing prerequisites and unsafe combinations.
- Run bounded execution: enforce timeouts, retry limits, loop detection and transaction or sandbox boundaries.
- Verify the result: test generated code, compare records before and after, validate business invariants and confirm task completion rather than tool-call completion.
- Record and review: persist the trace, approval decision, evidence and rollback information, then route exceptions to the accountable owner.
Earlier production lessons: latency, ambiguity and scale
A 2025 MLOps Community panel on voice agents added problems that apply to other AI systems: latency, accuracy, real-time processing, ambiguity and consistent user experience. Its participants noted that non-determinism makes testing and predictability difficult and that behavior changes dramatically when usage grows from a prototype to hundreds or thousands of conversations. Tom Shapland summarized the transition plainly: “There’s still a lot of craftsmanship that goes into taking your initial MVP and bringing it to something that’s production ready.”
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Implementation checklist for an accountable deployment
- Maintain an inventory of agents, tools, identities, data sources and owners.
- Separate read, write, deploy and administrative capabilities.
- Put high-impact actions behind an approval gate with evidence and rollback.
- Use schemas and policy checks at every agent-to-tool boundary.
- Set limits for retries, loops, time, cost and affected records.
- Monitor retrieval freshness, authorization failures, tool errors, task completion and anomalous behavior.
- Exercise incident drills that replay traces and revoke credentials.
- Review autonomy levels whenever the model, prompt, tool, data source or workflow changes.
The practical standard
Production AI should be judged by controlled authority and verifiable outcomes, not by fluent responses or a green demo. Harness engineering supplies the contracts, bounded orchestration, schemas and telemetry that make failures visible. Governance supplies the approval rules and accountable owner. Together they turn an agent from an opaque automation experiment into a system whose actions can be inspected, stopped, corrected and defended when real users and real data are involved.
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