Give your LLM agent fixed semantic tools - not another prompt.
Prompt-instructed safety checks inherit the model's variance and its vulnerabilities. This architecture puts Sentiqra's MCP server between the agent and its actions - deterministic detection tools the agent calls, and a policy layer you own that acts on the results.
Illustrative reference architecture. The scenario, volumes, and economics below are modeled - not a named customer case study. Validate every number against your own workload.
The scenario
A platform ships an AI support agent that reads inbound messages and drafts replies. The team added prompt instructions like "refuse abusive content" - but refusal behavior drifts with model updates, jailbreak attempts mutate instructions, and nobody can prove what the agent would have done on a given input last month.
Before / after architecture
Before
With Sentiqra
Decision ownership stays with you
- The agent proposes; your gateway disposes. Required checks are invoked by the gateway at the tool-execution boundary - an agent cannot bypass them by simply not calling the tool.
- Semantic analysis is evidence, not authorization: a benign result never grants access to a tool or permission to act - OWASP treats excessive functionality, permissions, and autonomy as distinct causes of harmful agent behavior.
- Approvals bind the exact proposed call - tenant, actor, tool, argument digest, policy version, validity window. Mutating arguments after approval invalidates it; analyzed text never supplies identity or policy.
- Tool calls are deterministic: the same text returns the same signals, replayable by configuration ID - behavior does not drift with LLM updates.
- Every analyzer run is metered like an API call - agent spend has the same quotas, caps, and audit trail as your other traffic.
Illustrative economics
Both columns are modeled from explicit assumptions. Do not treat these as guaranteed savings - model them against your own workload.
Current approach (modeled)
- Prompt-engineering time spent patching refusal behavior per model release
- + incident cost when the agent acts on manipulated or abusive input
- + audit cost: no replayable record of why the agent acted
With Sentiqra (modeled)
- N agent turns x tool calls per turn (only the checks you wire)
- = analyzer runs at current Sentiqra pricing, billed to the agent's key
- Policy logic lives in code you version - not in prompt prose
A guardrail check is one analyzer run - cheap next to the LLM completion it gates. Model calls-per-turn against your action policy. See current pricing.
Failure behavior
Enterprise architects care about what happens when analysis is unavailable almost as much as accuracy. Define these states explicitly.
Sentiqra available
Tool calls return structured signals in-band; the policy engine proceeds with evidence.
Analyzer latency spike
The agent waits or your policy applies a timeout default - define fail-open vs fail-closed per action type.
Sentiqra unavailable
Policy-defined: high-risk actions fail closed (queue for review), low-risk actions may proceed logged and flagged.
Quota / spend cap reached
Explicit 429-style state to the tool call - the agent cannot silently bypass the check.
Malformed tool call
MCP schema validation rejects it before analysis - no billable run, no ambiguity about what happened.
Illustrative operational impact
Checks that survive model upgrades
Swapping the LLM changes the agent's prose, not its guardrails - detection is external, deterministic, and versioned.
Prompt injection gets a second opinion
A manipulated instruction cannot edit the Cybersecurity or Trust & Safety signal - the check happens outside the model's context.
An audit trail per decision
Each tool call logs signals, configuration ID, and fingerprint - "why did the agent escalate this" has a replayable answer.
What to measure in your evaluation
Run both approaches against the same labeled production sample. Compare:
The right migration decision should come from your workload - not a generic savings percentage.
Build the reference architecture
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