The Tool Desk
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This matters because an agentic system can pursue a complex goal with limited direct supervision, rather than merely returning a single answer. OpenAI describes that class of system in its governance paper, Practices for Governing Agentic AI Systems. A prompt that says “be helpful” is therefore not a strategy. A usable strategy says who is served, what result counts as success, what authority is delegated, and where autonomy stops.
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What a strategic goal for an AI agent actually contains
A strategic goal connects organizational intent to an outcome a person can observe and verify. “Automate customer support” is too broad: it does not identify the customer, the supported cases, the systems involved, the quality bar, or the point at which a human must take over. A stronger goal is: “For authenticated customers, resolve password-reset requests by verifying account ownership, issuing only the approved reset workflow, and escalating any identity mismatch; 95% of completed cases must contain a recorded verification result and no account change may occur without that result.”
Write the goal in terms of the user’s job, not the model’s behavior. “Use a large language model to classify tickets” describes an implementation. “Return a correctly routed, policy-compliant answer or a human handoff within four minutes” describes an outcome. The implementation can change while the outcome and controls remain stable.
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Use observable success conditions
Every goal should have tests that a reviewer or automated evaluator can run. Include:
- Completion: the required artifact, decision, transaction or handoff exists in the designated system.
- Quality: factual accuracy, policy compliance, completeness, tone or other domain criteria meet a stated threshold.
- Time: the maximum response or processing time, including any human-review wait.
- Safety: prohibited actions, data disclosures and unauthorized changes are absent.
- Evidence: the trace, records or citations needed to verify the result.
Do not claim a numerical accuracy target unless you have a test set and a measurement method. A goal can initially use a qualitative acceptance rule, but the team should define how that rule will become measurable.
Define the boundary of the goal
List adjacent requests the agent must decline or route elsewhere. A procurement agent might compare approved vendors and prepare a purchase request, but it should not add a new vendor, alter banking details or approve its own request. Explicit exclusions prevent a broad instruction from being interpreted as permission to improvise.
The agent charter: a practical specification
Put the following fields in a versioned charter that engineers, security staff, operators and business owners can all review. The template synthesizes the implementation, governance and security guidance in OpenAI’s practical guide to building AI agents, its governance paper, and NIST’s work on agent identity and authority. It is a working specification, not a formal standard.
1. Purpose and user
Name the person, team or downstream system being served and the decision or job the agent supports. State the environment: production, staging, internal-only or customer-facing. Identify the accountable owner who can change the charter or stop the agent.
2. Success conditions
Describe the expected result, quality checks, timing, allowed cost or resource budget, and the evidence that proves completion. Include a “not completed” state so the agent can stop safely instead of fabricating success.
3. Scope and exclusions
Enumerate permitted task types and representative examples. Add explicit refusal and handoff cases, including requests outside the user’s authority, unsupported jurisdictions, missing data, and actions whose impact exceeds the approved risk level.
4. Permissions and resources
Inventory every data source, API, account, file store, browser session and execution environment. Assign the minimum read or write permission required for each task. Authentication establishes who is calling; authorization establishes what that identity may do. Keep those controls outside the model’s natural-language instructions. NIST’s February 2026 concept-paper announcement highlights identity and authorization for software and AI agents, but the announcement does not create a finalized requirement: NIST: New Concept Paper on Identity and Authority for Software Agents.
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5. Action boundaries
Classify actions before granting tools. A useful risk table is:
| Action class | Examples | Default control |
|---|---|---|
| Read-only | Search an approved knowledge base; retrieve a ticket | Allow within the user’s authorization; log the query and returned record identifiers. |
| Reversible change | Draft an email; create a private task; edit a recoverable document | Allow in a sandbox or draft state; require a review before publication or sending. |
| Sensitive change | Change access, disclose personal data, modify a customer record | Use field-level authorization, validation and explicit confirmation or human approval. |
| Financial or external commitment | Place an order, issue a refund, accept contract terms | Require an authorized human approval with a clear summary of amount, recipient and rationale. |
| Irreversible or high-impact | Delete records, close an account, deploy production code, make a regulated decision | Deny by default or require documented, independent human approval and a rollback or incident plan. |
For each tool, specify allowed operations, parameter ranges, destinations, data fields and maximum frequency. A tool that can send messages should not silently gain permission to delete records because both operations happen to use the same service account.
6. Input and output controls
Mark user text, retrieved pages, uploaded files, emails and tool responses as untrusted data. Represent commands and data separately so text found in a document cannot become an instruction merely because the model read it. Validate schemas, types, lengths, destinations and enumerated values before a tool call. On output, check that required fields exist, citations or source identifiers are present when required, and sensitive fields are not exposed to an unauthorized recipient.
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Define the maximum retries, time or token budget, confidence or risk triggers, escalation destination and safe-stop behavior. A failed tool call should not automatically be repeated forever or converted into a plausible-looking answer. Specify what the user sees when the agent cannot verify a fact or complete an action.
8. Evidence and review
Record the goal and charter version, authenticated principal, tools invoked, parameters after validation, data classifications, approvals, outputs, errors and final disposition. Preserve enough trace detail to reconstruct the decision without retaining unnecessary personal data. Define who reviews traces, how often evaluations run and which incidents trigger an immediate pause.
9. Change process
Require approval for changes to the goal, permissions, tools, model, prompts, guardrails and escalation routes. Evaluate the new version against regression cases and adversarial cases before expanding access. Keep old versions and their test results so an incident can be tied to the exact behavior in production.
Layer guardrails instead of trusting one filter
OpenAI defines guardrails as “the rules and safety mechanisms that define how an agent behaves” in its business guide, A Business Leader’s Guide to Working with Agents. In practice, no single prompt, classifier or approval screen is a complete security solution. Use independent layers that fail closed when possible.
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Enforce identity, authorization, network boundaries, allowlisted tools, parameter schemas, file and record permissions, destination restrictions, rate limits and transaction limits in code or policy middleware. Reject malformed or out-of-range requests before they reach an external system. Deterministic checks are especially important for money, access control, deletion and regulated data.
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Model-based checks
Use a separate review step to classify intent, detect policy-sensitive content, assess whether a proposed action matches the user’s request and identify prompt-injection patterns. Keep the reviewer’s authority narrow: it may block or escalate, but it should not be able to bypass the same authorization policy it is reviewing. Test the reviewer for false negatives and false positives and provide a human route when uncertainty is material.
Structured data paths
Prefer typed objects, enumerated action names and explicit fields over free-form text passed directly to tools. Validate both the agent’s proposed call and the tool’s response. Escape or isolate markup and instructions in retrieved content. If a downstream API accepts only a small set of values, make that set part of the schema rather than asking the model to remember it.
Isolation and least privilege
Run risky work in a sandbox, separate tenant data, scope credentials to one job and expire them quickly. Use service identities that identify the agent and the human or system principal on whose behalf it acts. Log both identities so an agent cannot become an untraceable shared account.
Choose autonomy in proportion to impact
Autonomy is not a single yes-or-no setting. Increase it only when the agent demonstrates reliable goal completion and boundary compliance under realistic and adversarial tests. Compare each candidate level on task success, impact and reversibility, permission breadth, human oversight, security exposure, auditability and operational burden. This incremental approach is consistent with the practical guidance from OpenAI, NIST’s Agentic AI work and OWASP’s Agent Control Standard resource; OWASP’s resource describes runtime middleware hooks and portable policy enforcement, but its maturity and adoption should be checked before treating it as universal.
| Autonomy level | What the agent may do | When it fits | Required safeguards |
|---|---|---|---|
| Observe | Read approved data and produce analysis; no external changes | New agents, high-uncertainty work, sensitive environments | Read-only credentials, source checks, complete trace |
| Recommend | Prepare a decision, draft or tool call for a person | When judgment is useful but consequences matter | Human review of the complete proposed action and evidence |
| Bounded execute | Perform pre-approved, reversible actions within strict limits | Stable, well-tested workflows | Allowlisted operations, schemas, quotas, rollback and sampling review |
| Conditional execute | Act automatically on low-risk cases and escalate exceptions | High-volume workflows with clear risk signals | Real-time risk checks, approval triggers, short-lived credentials and incident stop switch |
| High-impact execute | Take irreversible, financial or regulated actions | Only where governance, evidence and independent approval justify it | Explicit human authorization, dual control where appropriate, strong audit and tested recovery; never assume the model’s confidence is authorization |
A useful rule is to move one level at a time. Expand tool scope or remove an approval gate only after the existing level meets its success and safety tests across a representative evaluation set, including rare edge cases.
Step-by-step method for defining and deploying the agent
- Map the workflow. Interview users and operators, draw each decision and handoff, and mark data classifications, external effects and points where a mistake is costly or hard to reverse. Record the current human process rather than designing from a blank prompt.
- Write the outcome and refusal cases. State the user, artifact or transaction, quality bar, time limit and evidence. List requests that must be declined, deferred or routed to a named queue.
- Inventory tools and authority. For every data source and API, document owner, environment, operations, credential, retention, rate limit and allowed fields. Remove tools that are convenient but not necessary. Bind each operation to the authenticated user’s authorization.
- Classify actions by impact. Mark read-only, reversible, sensitive, financial, public-facing and irreversible actions. Set confirmation, approval, dual-control or denial requirements before writing prompts or workflows.
- Design the control layers. Implement identity and authorization first, then schema validation, destination and quota checks, content isolation, model-based review and human approval. Make the safe path the default when a check is unavailable.
- Define failure and recovery. Set retry and loop limits, timeouts, maximum spend or number of records, escalation queue, user-facing error, rollback procedure and an emergency disable switch. Test that a partial failure does not leave an untracked or duplicated transaction.
- Build evaluations. Create normal cases for each supported task, boundary cases for every exclusion, adversarial cases with malicious or misleading instructions, and regression cases from previous incidents. Score both the user outcome and the policy outcome: an answer that solves the task by leaking data is a failure.
- Run in observe or recommend mode. Compare proposed actions with expert decisions, inspect traces and measure false approvals, unnecessary escalations, latency and resource use. Do not infer production safety from a handful of successful demonstrations.
- Release gradually. Start with a small user group, narrow data and reversible actions. Set alert thresholds and a named on-call owner. Expand only after the evidence supports the next autonomy level.
- Review and revise. Sample completed runs, investigate near misses, update tests and change the charter under version control. Revoke unused permissions and re-evaluate after model, tool, policy or workflow changes.
Prevent common unsafe behaviors
Prompt injection and instruction confusion
An email, web page, ticket or uploaded file may contain text that attempts to redirect the agent. Treat retrieved content as data, not authority. Keep system policy and tool permissions outside the retrieved text, delimit untrusted content, strip active markup where appropriate, and require a policy check before any action derived from it. A human approval screen should show the source content and the exact proposed operation, not only the agent’s summary.
Data leakage
Enforce access at retrieval and delivery. Filter results by the requesting principal, minimize fields, redact secrets and prevent an agent from copying data from one tenant or purpose into another. Log disclosure decisions and test indirect leakage through summaries, error messages and tool parameters.
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Confused-deputy actions
An agent may possess a service credential that is more powerful than the user’s authority. Pass the user or calling service identity through every authorization decision, use scoped tokens and reject requests when the delegation chain is missing or ambiguous. Never let a model-generated string select an arbitrary account, project or destination.
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Excessive agency and tool chaining
Limit the number, type and order of actions. Require a fresh check when the plan crosses a risk boundary, changes the target, or uses new data. Cap recursion, retries and parallel calls. A successful first step must not grant permission for later steps that were never approved.
Hallucinated completion
Require a verifiable transaction identifier, source record, test result or human confirmation before marking a task complete. If a tool is unavailable or returns ambiguous data, report the uncertainty and escalate rather than inventing a result.
Runaway cost or denial of service
Set per-run and per-user budgets, concurrency limits, timeouts and circuit breakers. Alert on unusual tool-call volume or repeated failures. Preserve a safe, low-cost fallback such as a queue for human handling.
Design human approval that people can actually use
Approval is meaningful only when the reviewer can understand and control the proposed action. Present the requested operation, target, affected records, data to be disclosed, amount or other impact, reason, sources, policy checks and reversibility in one view. Offer approve, edit, reject and escalate choices; do not turn approval into a blind confirmation of a long generated paragraph.
Set approval triggers in the charter: sensitive fields, new recipients, unusual amounts, policy uncertainty, low evaluation confidence, conflicting sources, irreversible operations and any deviation from the normal workflow. Record approver identity, time, charter version, exact payload and outcome. Expire stale approvals when the target, amount or payload changes.
Measure both outcomes and boundaries
A dashboard that shows only task completion can reward unsafe behavior. Pair business metrics with control metrics:
- Outcome: completion rate, quality score, latency, escalation rate and user correction rate.
- Boundary: blocked unauthorized calls, policy violations, sensitive-data exposure attempts, approval bypass attempts and actions outside the allowlist.
- Reliability: tool errors, retries, loops, timeouts, duplicate transactions and rollback events.
- Operations: trace completeness, alert response time, cost per run, credential failures and queue age.
Review representative traces, not only aggregate numbers. Sample successful runs because a policy breach can be hidden inside an apparently successful outcome. Maintain a fixed regression set and add incidents, near misses and newly discovered attack patterns. OpenAI’s agent safety documentation emphasizes structured outputs, approvals, guardrails and trace evaluation; those practices remain useful even as individual products change.
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Governance, standards and changing platform details
Agent security builds on established cybersecurity: asset inventory, identity, least privilege, secrets management, network segmentation, logging, vulnerability management, incident response and recovery. It adds agent-specific concerns such as untrusted instructions in data, dynamic tool selection, delegated authority and plans that evolve during execution. NIST’s May 18, 2026 summary of responses on security considerations for AI reports commenters’ concerns and proposed mitigations; it is a summary of submitted responses, not a binding standard.
NIST announced an AI Agent Standards Initiative on February 17, 2026, focused on industry-led standards, open-source protocol development and research into security and identity: NIST announcement. Planned deliverables may evolve. The agency’s February 5, 2026 identity-and-authority announcement likewise describes a concept paper and notes that its public-feedback deadline has passed; it should not be read as a finalized compliance requirement.
Product lifecycle information can change faster than governance principles. OpenAI documentation currently says Agent Builder is scheduled to shut down on November 30, 2026. Verify that date and any migration details before relying on them: OpenAI agent safety documentation. Keep your charter, tests and policy layer portable so a platform change does not silently remove an approval, log or authorization check.
Worked example: an accounts-payable invoice agent
Goal: For invoices received from approved suppliers, extract fields, match the purchase order and prepare a payment recommendation within one business day. Completion requires a stored invoice image, extracted values, match result and cited source records.
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Controls: Optical extraction produces a typed object with supplier, invoice number, currency, amount and purchase-order identifier. Schema and arithmetic checks run before matching. A separate policy check flags new bank details, duplicate invoice numbers, tax discrepancies and amounts above the approved threshold. Any flagged case goes to an accounts-payable reviewer. The agent has read-only credentials plus permission to create an internal draft case; credentials expire after the run.
Evidence and failure: The trace records the authenticated mailbox identity, document hash, source-record identifiers, extracted values, checks, reviewer decision and charter version. After two failed extraction attempts or an ambiguous match, the agent stops and routes the invoice to the exception queue. It cannot mark the invoice paid, and a human can disable the workflow without changing application code.
Checklist before granting production autonomy
- The goal names a user, outcome, quality bar, time limit and completion evidence.
- Supported tasks and explicit exclusions are written and tested.
- Every tool, data source, account, operation and credential has an owner and least-privilege scope.
- Actions are classified by reversibility and impact, with approval rules for sensitive and irreversible work.
- Untrusted inputs and tool results are isolated from instructions and validated against schemas.
- Identity, authorization, quotas, retries, timeouts and circuit breakers are enforced outside the model.
- Human reviewers see the exact proposed action and can edit, reject or escalate it.
- Evaluations cover normal, boundary, adversarial and regression cases and score safety as well as task success.
- Traces identify the user, agent, charter version, tools, parameters, approvals, outputs and errors.
- A named owner monitors alerts, handles incidents, approves changes and can stop the agent.
The practical test is simple: can an independent reviewer explain what the agent was trying to achieve, what authority it had, why each consequential action was allowed, and how the organization would recover if it was wrong? If not, the agent has a prompt, not a governable strategy.
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