LLM Evaluation Service
Use when the primary decision concerns model or LLM-enabled application quality, groundedness, safety, robustness and release evidence rather than the complete agent action loop.
View related service ↗ AI Assurance & Evaluation
AI Agent Evaluation for Reliable, Safe Production Decisions
Evaluate autonomous and semi-autonomous AI agents across task completion, action trajectories, tool and API use, grounding, memory, permissions, safety, security, recovery, cost and operational evidence before production authority expands.
Risk-based scenarios and acceptance criteria
Human and automated evaluation methods
Security, privacy and permission-boundary tests
Traceable findings, remediation and release evidence
Scope, evaluation depth, environment access, timeline and commercial terms are confirmed after discovery.
Agent system evaluation viewEvaluation active
User GoalIntent, constraints and success criteria
Agent PlanTask decomposition and decision path
Context & StateRetrieval, memory and session context
Tool UseSelection, arguments and permission checks
ActionExecution, verification and recovery
OutcomeTask result, impact and completion evidence
Evaluation controls & observability
Guardrails & policy Memory & state Permissions & access Monitoring & alerts
Evaluation evidence
Test resultsTraces & logsSafety checksRisk assessmentRelease recommendation
Risk-based test designScenarios mapped to consequences and controls
Human + automated reviewRepeatability with calibrated judgement
Security & governance checksPermissions, policy, privacy and misuse paths
Repeatable release evidenceFindings linked to remediation and retesting
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Why AI Agent Evaluation Matters Before Authority Reaches Production
An agent can produce a convincing final answer while taking the wrong path underneath. Evaluation therefore needs to examine the complete decision and action trajectory, not only response quality.
Looks good in a demo
Wrong tool selection
Incorrect parameters
Permission overreach
Unsupported actions
Hidden state errors
Unsafe output
High latency or cost
Poor escalation
Unreliable in production
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Move From Informal Testing to Traceable Production Evidence
The target is not a universal “safe” score. It is a documented evaluation approach in which scenarios, acceptance thresholds, evidence, residual risks and release decisions are explicit for the intended use.
Current state
High uncertainty- Informal demonstrations
- Anecdotal happy-path testing
- Model-only or output-only metrics
- Limited trace visibility
- Unclear release decision ownership
Target state
Production decision ready- Defined risk-based scenarios
- Traceable test cases and evidence
- End-to-end agent evaluation
- Risk-weighted findings and owners
- Explicit acceptance and release gates
- Reusable regression suite
- Ongoing monitoring and re-test triggers
3
What Our AI Agent Evaluation Service Covers
Coverage is selected according to intended use, autonomy, impact, system architecture, data sensitivity and the evidence required for the deployment or governance decision.
Task & Outcome Quality
Completion, correctness, constraints and business usefulness
Reasoning & Trajectory
Decision path, loops, unnecessary steps and failure propagation
Tool & API Use
Selection, arguments, schema, execution and verification
Grounding & Knowledge
Retrieval relevance, evidence use, freshness and abstention
Safety & Security
Misuse, injection, unsafe actions and control effectiveness
Privacy & Permissions
Data exposure, least privilege, identity and access boundaries
Memory & State
State consistency, contamination, retention and cross-session behaviour
Human Oversight
Confirmation, escalation, handoff and intervention quality
Performance & Cost
Latency, repeated calls, efficiency and cost per successful task
Governance & Evidence
Traceability, decision records, residual risk and monitoring obligations
4
Evaluation Framework: Assess the Agent as a System, Not a Single Model
Evaluation dimensions are connected because one failure can propagate across planning, tools, state, permissions and final outcomes. Measures and thresholds are defined for the client’s system rather than imposed as generic scores.
Task Success
Correctness, completeness and completion under stated constraints.
Groundedness
Evidence use, source attribution, freshness and unsupported claims.
Instruction Following
Alignment with user, system, policy and operational constraints.
Tool Use
Correct tool, valid parameters, permission checks and outcome verification.
AI Agent
Evaluation
Evaluation
State & Memory
Consistency, persistence, contamination and state-transition behaviour.
Security & Safety
Injection, misuse, leakage, harmful actions and safeguard response.
Policy Adherence
Permission boundaries, prohibited behaviour and required approvals.
Latency & Cost
Response time, retries, token/tool consumption and successful-task cost.
Recovery & Escalation
Error handling, rollback, fallback, handoff and human intervention quality.
Auditability
Trace completeness, evidence reproducibility and decision explanations.
5
Risk & Readiness Assessment Makes Release Conditions Explicit
A readiness view can summarise where evidence is strong, where monitoring is required and where remediation is needed. The example below is illustrative; actual dimensions, ratings and thresholds are agreed for the engagement.
| Evaluation dimension | Low risk | Watch | Needs attention | Evidence state |
|---|---|---|---|---|
| Task completion | Criteria met | Monitor | Block if material | Trace + result |
| Tool accuracy | Controlled | Review | Remediate | Calls + arguments |
| Grounding & factuality | Supported | Review | Remediate | Sources + rubric |
| Security & safety | Controls hold | Review | Remediate | Scenario evidence |
| Policy adherence | Within boundary | Review | Remediate | Policy trace |
| Recovery & error handling | Recoverable | Review | Remediate | Failure trace |
| Human handoff | Escalates | Review | Remediate | Handoff evidence |
| Monitoring & observability | Traceable | Review | Remediate | Logs + alerts |
Illustrative assessment structure only. A production decision should use agreed acceptance criteria, evidence limitations, responsible owners and residual-risk treatment.
From Business Objectives to Release Decisions
Business objective
What outcome is the organisation trying to achieve?
User goal
What does the user ask the agent to do?
Agent decision
How should the agent plan and reason?
Tool / action
Which tools, APIs or actions are permitted?
Control requirement
What must be prevented, detected or approved?
Test scenario
How will the required behaviour be challenged?
Evaluation method
What deterministic, model-based or human measure applies?
Acceptance threshold
What is considered good enough for this risk?
Evidence
What proof must be retained?
Release decision
Deploy, restrict, remediate, re-test or defer?
6
Agent System Test Architecture Places Evaluation Probes at Every Material Layer
A production agent is a connected system. Testing should capture evidence across inputs, orchestration, models, retrieval, memory, tools, enterprise systems and the controls that govern access and action.
Users & inputs
Business events & system triggers
Evaluation probes at each layer: events, decisions, traces, inputs, outputs and control outcomes
Orchestrator / plannerTask decomposition, policy routing and decision sequence
Model(s)Foundation, fine-tuned, routed or multimodal models
Retrieval / memoryKnowledge, state, context, vector search and session memory
Tools / APIsEnterprise actions, functions, MCP tools and external services
Enterprise systems
CRM, ERP, databases & external services
Permissions & access · policy & guardrails · security controls
Observability & logging · audit trail · version evidence
Human approval · escalation · rollback & recovery
7
Safety, Security & Control Testing Goes Beyond Output Moderation
For agents with tools, data access or operational authority, evaluation can test whether prevention, detection, human approval and audit evidence work together under realistic misuse and failure conditions.
| Risk area | Prevention | Detection | Human approval | Audit & re-test |
|---|---|---|---|---|
| Prompt injection | ✓ | ✓ | Review | ✓ |
| Jailbreak attempts | ✓ | ✓ | Review | ✓ |
| Data leakage | ✓ | ✓ | ✓ | ✓ |
| Excessive agency | ✓ | ✓ | ✓ | ✓ |
| Unauthorised tool use | ✓ | ✓ | ✓ | ✓ |
| Destructive actions | ✓ | ✓ | ✓ | ✓ |
| Hidden prompt or secret exposure | ✓ | ✓ | Review | ✓ |
| Sensitive-data handling | ✓ | ✓ | ✓ | ✓ |
| Privilege escalation | ✓ | ✓ | ✓ | ✓ |
| Unsafe external action | ✓ | ✓ | ✓ | ✓ |
The matrix shows control categories that may be evaluated, not a claim that any particular client system already implements them. Security scope is authorised and agreed before testing.
Risk-informed reference points
Where relevant, evidence can be mapped to the organisation’s own policies and recognised reference points such as NIST AI RMF, the NIST Generative AI Profile, ISO/IEC 42001 management-system requirements and current OWASP agentic AI security guidance. This supports structured review; it is not a certification or legal opinion.
Authorised test boundaries
Adversarial, tool and permission testing should use approved environments, accounts, data, rate limits and stop conditions. Destructive or external side effects are not assumed to be in scope.
Evidence limitations remain visible
Findings should identify the version, environment, scenarios, data, access and controls tested so decision-makers can distinguish demonstrated behaviour from untested assumptions.
8
Tool-Use & Action Evaluation Follows the Complete Intent-to-Verification Chain
Testing should separate whether the agent chose the right tool from whether it formed valid arguments, had appropriate authority, executed correctly, verified the result and recovered safely when something failed.
Intent recognition
Plan
Tool selection
Parameter construction
Permission check
Confirmation gate
Execution
Verification / recovery
Wrong toolIncorrect parametersPermission overreachFailed executionIncorrect result interpretationNo recovery
Test Data & Scenario Design
Coverage should reflect realistic use, not only convenient examples.
- Happy-path scenarios
- Missing or stale context
- Edge cases and boundary conditions
- Multi-step and long-horizon tasks
- Adversarial and red-team scenarios
- Partial success and recovery
- Ambiguous or incomplete requests
- Domain-specific scenarios
- Conflicting instructions
- Compliance and policy scenarios
- Tool failure and system errors
- Production-like de-identified data
What We Need From Your Environment
Evaluation quality depends on representative evidence and appropriate access.
- Agent purpose and user journeys
- Architecture and dependency map
- Model, prompt and version information
- Tool/API definitions and schemas
- Permission and identity model
- Representative tasks and expected outcomes
- Test or sandbox environment
- Logs, traces and observability fields
- Policies, prohibited actions and approvals
- Known incidents and failure examples
- Domain reviewers where judgement is needed
- Accountable release decision owners
9
Prioritise Findings by Business Impact, Exploitability and Release Criticality
A useful evaluation does more than enumerate failures. Findings should be connected to impact, likelihood or exploitability, affected workflows, available controls, remediation effort and the decision that must be made.
Illustrative prioritisation matrix
ImportantPlan fix and track to an agreed milestone
CriticalFix before release or materially restrict authority
LowMonitor, document and re-evaluate if context changes
ModerateFix in the next release or add compensating controls
Factors considered
Business impact
Customer, financial, operational, legal, safety and reputation consequences.
Severity & exploitability
How material the failure is and how easily it can be triggered or abused.
User harm & control depth
Who may be affected and whether prevention, detection or approval controls reduce risk.
Remediation & release criticality
Implementation effort, dependency, retest need and whether the issue blocks production.
Transformation & remediation roadmap
1Align & define criteriaUse case, risks, acceptance rules
2Build test suiteScenarios, data, rubrics, automation
3Execute evaluationAutomated + human review
4Analyse failuresRoot cause, impact and control gaps
5Remediate controlsPrompt, workflow, access and guardrails
6Re-test & regression gateValidate fixes and baseline behaviour
7Production monitoringWatch drift, incidents and material change
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Tangible Deliverables Turn Test Results Into Reusable Assurance Assets
Final outputs are agreed during discovery. The objective is to leave decision-makers and engineering teams with evidence that can be reviewed, acted on, re-tested and reused after the engagement.
DELIVERABLE 01
Evaluation strategy & plan
Scope, system boundary, risks, metrics, thresholds, roles and decision logic.
DELIVERABLE 02
Risk model & assessment
Risk hypotheses, failure consequences, controls and prioritisation criteria.
DELIVERABLE 03
Scenario & test dataset
Representative, edge, adversarial, failure, recovery and policy scenarios.
DELIVERABLE 04
Automated evaluation harness
Where in scope, repeatable assertions, graders, test runners and version controls.
DELIVERABLE 05
Failure taxonomy & analysis
Trace-backed failure classes, patterns, root causes and affected workflows.
DELIVERABLE 06
Tool-use findings
Selection, parameters, permission, execution, verification and recovery issues.
DELIVERABLE 07
Safety & security findings
Evidence from authorised misuse, injection, leakage and boundary scenarios.
DELIVERABLE 08
Scorecard & error taxonomy
Measures, human review, disagreement handling, severity and limitations.
DELIVERABLE 09
Remediation backlog
Prioritised fixes, owners, dependencies, control changes and retest criteria.
DELIVERABLE 10
Regression test pack
Reusable baselines and scenarios for model, prompt, tool or workflow change.
DELIVERABLE 11
Release-readiness report
Coverage, unresolved findings, residual risks, conditions and recommendation.
DELIVERABLE 12
Monitoring & governance pack
Signals, thresholds, review triggers, evidence ownership and decision records.
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Our Delivery Methodology Keeps Criteria, Evidence and Remediation Connected
The sequence is adapted to system maturity, access and risk. A fixed duration is not assumed before the evaluation boundary and evidence needs are understood.
1Understand use casePurpose, users, consequences and owners
2Define risks & criteriaRequired behaviour and prohibited outcomes
3Build scenariosRepresentative and risk-weighted test coverage
4Run evaluationAutomated tests, traces and human review
5Analyse failuresRoot cause and materiality assessment
6Prioritise remediationOwners, controls, dependencies and retest
7Re-test & evidenceValidate change and prepare release evidence
Independent assurance
Second-line evaluation review
Objective review of internal testing, controls, unresolved risks, evidence quality and release readiness.
- Evidence review and gap analysis
- Independent scenario challenge
- Risk-ranked findings
- Decision-ready report
Embedded support
Evaluation engineering
Specialists work with product and engineering teams to establish repeatable evaluation assets and release gates.
- Scenario and metric design
- Test harness and grader support
- Trace analysis and remediation
- Regression gate enablement
Ongoing service
Evaluation operations
Recurring regression, monitoring review, evidence reporting and periodic risk reassessment after release.
- Test maintenance and expansion
- Version and regression review
- Monitoring and incident evidence
- Periodic assurance reporting
12
AI Agent Evaluation Pricing Is Scope-Led; Public Market References Help With Early Budgeting
No approved fixed DataConsultant fee was available for this service in the sources used for this page. DataConsultant pricing should therefore be confirmed through a scoped proposal. The public INR figures below are independent market references, not DataConsultant prices.
Indicative Market Pricing (INR)
Comparable independent AI evaluation work
₹1.5 lakh – ₹6 lakh+Current public India pricing shows a ₹1.5 lakh starting point for a focused AI agent quality assessment and a ₹3–6 lakh range for an independent LLM/AI reliability assessment. The scopes are comparable because both centre on structured evaluation, repeatable evidence, quality/reliability testing and production-readiness decisions, but real agent engagements can vary materially with autonomy, integrations, security depth and remediation cycles.
This is market guidance for scoping only and is not an official published DataConsultant fee. A written DataConsultant estimate should follow discovery.
What affects scope, timeline & price
Number of agents & workflowsAgents, roles, steps, handoffs and deployment environments.
Tools & integrationsAPIs, MCP tools, enterprise systems and external actions.
Autonomy & consequenceAuthority level, customer impact and potential downstream harm.
Scenario coverageNormal, edge, failure, adversarial and recovery scenarios.
Data & environment readinessTest data, logs, traces, sandboxes and production-equivalent controls.
Security & privacy depthInjection, leakage, identity, permissions and authorised red-team coverage.
Human-review depthDomain expertise, calibration, adjudication and independent review needs.
Remediation & continuityEngineering support, re-test cycles, regression automation and monitoring.
Timeline confirmed after scoping. Third-party model, cloud, observability or testing-platform consumption is separate from consulting scope unless explicitly included in the proposal.
13
Use AI Agent Evaluation When the Decision Depends on End-to-End Behaviour
A focused agent evaluation is most valuable when the system can choose tools, retain state, take actions or affect users and business processes. A narrower adjacent service may be better when the question is limited to one component.
Good fit for AI agent evaluation
- The agent performs multi-step tasks rather than returning a single model response.
- Tools, APIs, memory or enterprise data are involved in task completion.
- The agent can create external side effects or make operational decisions.
- Product, risk or governance teams need evidence for a release gate.
- Existing tests focus on happy paths or final text rather than trajectories.
- You need a regression suite for future model, prompt, tool or workflow changes.
A different or additional service may be needed
- The decision is primarily about comparing foundation models rather than an end-to-end agent.
- The requirement is a specialist penetration test or statutory certification as the sole deliverable.
- The main problem is retrieval quality, output factuality or bias and requires deep specialist evaluation.
- The system boundary, intended use and accountable owner have not yet been defined.
- No representative scenarios, evidence or environment can be made available.
- You expect evaluation to guarantee that a probabilistic system can never fail.
14
Why Consider DataConsultant for AI Agent Evaluation
The value of an independent evaluation is the quality of the decision evidence it creates. The service is structured around business purpose, system behaviour, controls, transparent limitations and practical handover.
Business-led acceptance criteria
Translate intended outcomes, prohibited consequences and risk appetite into testable behaviours instead of relying on generic benchmark scores.
End-to-end system view
Evaluate the connected agent stack—planning, models, retrieval, memory, tools, permissions, users and operating controls—not only the final text response.
Governance by design
Connect tests and findings to decision rights, approval routes, control evidence, residual risk and re-test triggers.
Platform-aware, requirements-led
Work with the client’s architecture and tools without forcing a particular model, evaluation platform or vendor as the answer.
Traceable evidence & limitations
Document scenarios, versions, environments, failures and evidence boundaries so reviewers can understand what was and was not demonstrated.
Knowledge transfer & continuity
Retain scenarios, rubrics, regression assets and operating guidance so internal teams can continue evaluation as the agent changes.
16
AI Agent Evaluation Frequently Asked Questions
Answers for product, engineering, technology, security, privacy, risk, compliance, audit, procurement and governance teams evaluating production-readiness needs.
What is AI agent evaluation?
AI agent evaluation is structured testing of an autonomous or semi-autonomous AI system across the complete task trajectory: goal interpretation, planning, reasoning signals, retrieval, memory, tool selection, arguments, permissions, actions, outputs, recovery, escalation and downstream outcomes. The purpose is to replace informal demonstrations with repeatable evidence and explicit release criteria.
How is AI agent evaluation different from LLM evaluation?
LLM evaluation focuses on model or application behaviour such as correctness, groundedness, relevance, safety and robustness. Agent evaluation adds the operational action layer: planning, multi-step state, tool and API use, permissions, side effects, recovery, human approval, cost, latency and traceability across the complete workflow.
What does DataConsultant evaluate in an AI agent?
Scope can include task completion, outcome quality, planning and trajectory behaviour, grounding, tool selection and parameter accuracy, memory and state, permission boundaries, policy adherence, safety, security, privacy, failure recovery, human escalation, latency, cost, observability, audit evidence and production-readiness controls. Final scope is defined against the agent’s intended use and risk.
When should an organisation commission an AI agent evaluation?
Useful points include before a pilot, before production approval, before expanding an agent’s authority, users, tools or data access, after material model or workflow changes, after an incident, before procurement approval, and when a recurring regression or monitoring process is being established.
What inputs are needed to start?
Useful inputs include the agent’s business purpose, user groups, architecture, models, prompts, tool and API inventory, permissions, retrieval sources, memory design, representative tasks, known failure modes, logs or traces, policies, risk requirements, expected human approvals, deployment environments and accountable stakeholders. Missing evidence is recorded as a limitation rather than assumed.
Can evaluation begin before production access is available?
Yes. Early evaluation can use prototypes, sandboxes, mocked tools, synthetic or de-identified data, recorded traces and design evidence. Conclusions should remain bounded by the environment tested, and production-equivalent permissions, integrations, data flows and controls should be evaluated before relying on the findings for a final release decision.
How are automated judges and human reviewers used?
Automated checks can provide repeatable coverage for deterministic rules, trace assertions, structured outputs, regression and scalable scoring. Model-based judges can support broader evaluation but require calibration. Human review is used where domain judgement, ambiguity, safety context, severity or adjudication cannot be reduced reliably to an automated rule.
Does AI agent evaluation include prompt injection and tool misuse testing?
It can. Where relevant, scenarios may assess direct and indirect prompt injection, unsafe tool selection, incorrect arguments, excessive permissions, identity and privilege misuse, data leakage, untrusted tool output, destructive actions, hidden-state failures and weak human escalation. Dedicated adversarial or privacy and security testing can be added when deeper coverage is required.
What deliverables can we receive?
Typical outputs can include an evaluation charter, system and risk map, scenario library, test cases, evaluation dataset or fixtures, scoring rubrics, automated test harnesses where in scope, trace and failure analysis, risk and control findings, remediation backlog, regression suite, production-readiness report, monitoring thresholds and a governance decision pack.
How long does an AI agent evaluation take?
Timeline is confirmed after scoping. It depends on the number of agents and workflows, tools and integrations, risk level, scenario coverage, environment readiness, test-data preparation, trace availability, specialist human-review depth, stakeholder access and whether remediation and re-testing cycles are included.
How is AI agent evaluation pricing handled?
DataConsultant does not publish a fixed fee for this service in the approved information used for this page. Pricing is scoped to agent complexity, autonomy, risk, number of workflows and environments, tools and integrations, scenario volume, security depth, test-data preparation, human review, reporting, remediation, regression automation and ongoing monitoring requirements. Public market references are shown only as indicative scoping context and are not DataConsultant fees.
Does evaluation guarantee that an AI agent will never fail?
No. Evaluation reduces uncertainty by testing defined scenarios and controls, but it cannot prove universal reliability for a probabilistic system. Unseen inputs, model changes, third-party tools, data changes and operating conditions can create new behaviour, which is why version control, regression testing, monitoring and incident learning are important.
Can DataConsultant support remediation and ongoing evaluation?
Yes. Follow-on work can include test-driven remediation, prompt and workflow changes, guardrail and permission improvements, observability, regression automation, re-testing, release gates, periodic independent review, monitoring design, managed evaluation operations and knowledge transfer. Responsibilities and acceptance criteria are agreed during scoping.
Evaluation scoping
Tell Us What Your AI Agent Can Do—and What Must Never Go Wrong
Share enough context for a practical first view of evaluation scope. Do not submit passwords, API secrets, production credentials, payment details or highly sensitive data through this public form.
- Agent purpose, users and deployment stage
- Models, tools, APIs, memory and enterprise integrations
- Known failure modes, incidents or control concerns
- Release, procurement, assurance or monitoring decision needed
- Required evidence, environments and stakeholder groups
Request an AI Agent Evaluation Scope Review
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