Agentic tool calls
Agentic systems rely on LLMs that autonomously trigger actions, call tools, pass data to external systems, and coordinate with other models. Each step introduces operational risk. Teams must score tool calls for correctness, safety, compliance, and overall task efficacy. They also need a way to interrupt or escalate when an LLM makes a risky or inefficient choice.
Traditional guardrail frameworks are too slow and too generic for real time agentic operation. LLM based evaluators add unacceptable latency. Static rules cannot adapt to new tools, workflows, or unexpected states.
Orca provides a low latency, adaptive evaluation layer designed specifically for agentic tool ecosystems.
Why tool call instrumentation is difficult today
LLM based scoring is too slow
Using an LLM to evaluate an agent’s tool decision adds hundreds of milliseconds or more. This breaks real time operation and causes user-visible delays
Lack of precise, contextual scoring
Tool call quality depends on many factors including correctness, safety, compliance relevance, routing accuracy, and state of the workflow. Hard coded rules or prompt based constraints cannot capture all of these
No mechanism to adjust to new tools or workflows
When new tools or APIs are added, teams must manually update prompts, policies, or models. This slows iteration and increases exposure to errors
No clear audit trail
Engineering and safety teams often cannot explain why a tool call was allowed or rejected. This complicates debugging and compliance review
Orca's solution:
Real-time evaluation for every tool call
1. Ultra low latency scoring
Orca evaluates each tool call in tens of milliseconds, far faster than LLM based supervision. This supports real time agentic decision loops at scale
2. Real time adaptation through memory control
Orca updates its behavior immediately when new examples or constraints are added to the memoryset. This allows teams to evolve tool call policies without retraining
3. Per call customization
Each tool call can load a different memoryset based on agent role, workflow phase, user profile, security context, or tool type. This removes the need for separate models or complex branching logic
4. Deterministic scoring and explainability
Orca provides clear references to the memories that influenced each scoring decision. Engineers can inspect and correct logic without guesswork
5. Robust handling of out of distribution calls
Orca retrieval based classifiers maintain accuracy even when the agent triggers new or unusual tool sequences. This avoids silent failure modes common in static classifiers
What you can score for each tool call
- Correctness and success likelihood
- Compliance risk
- Safety and harmfulness checks
- Data governance and PII checks
- Workflow alignment and routing accuracy
- Tool misuse or out of scope actions
- Need for human review or escalation
- Cost awareness and resource usage
- Detection of unexpected or incoherent agent behavior
Example workflow
1. Agent chooses a tool to call
2. Orca evaluates the call, arguments, and context in real time
3. If safe and valid, the tool call proceeds
4. If risky or incoherent, Orca can block, substitute, or trigger human review
5. Engineers inspect any misclassification through the inspector and update memory immediately
Where this fits
- Agentic orchestration frameworks
- Customer support automation
- Retrieval augmented agent workflows
- Complex multi agent systems
- Enterprise AI systems that require tight safety and compliance guarantees
- Any environment where tool misuse introduces cost, safety, or reputation risk
Talk to Orca
Speak to our engineering team to learn how we can help you unlock high performance agentic AI / LLM evaluation, real-time adaptive ML, and accelerated AI operations.