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Define agent identity, model selection, temperature, and system prompt per deployment. Version and clone agents for A/B evaluation.
Attach structured, reusable skill blocks—prompt fragments and logic units—that extend agent capability without modifying the base model.
Connect agents to external tools and APIs via the Model Context Protocol. Register, test, and scope tools per agent instance.
Every FoxAdmin agent is a fully configurable runtime — not just a prompt wrapper. You control the model, the reasoning strategy, the tool surface, the knowledge it can access, and the guardrails that govern what it's allowed to do. Agents can operate in a tight request-reply loop or run autonomously across dozens of tool calls until a goal is reached.
Skills are modular prompt blocks attached to agents to shape their behaviour without touching the base system prompt. They are organized into four categories — each addressing a distinct layer of agent operation.
Controls how the agent talks to users. Examples: tone presets (professional, concise, condescending), conversational expansion to suggest follow-up topics, and output formatting rules.
Governs how the agent thinks and plans. The Agentic Operator enables autonomous multi-step tool chaining; Research Analyst Standards enforce accuracy, traceability, and structured output.
Bundles specific MCP tool sets into a reusable unit. Attaching a capability skill wires the agent to a pre-scoped set of external tools — e.g. a Google Drive suite or the full QMS evaluation stack.
Detects and counters unsafe conditions at runtime. Skills watch for hallucination caused by iteration limits, prompt injection in user input, and RAG injection embedded in retrieved documents.
All tools run via the skunkbox-mcp server. Agents call them autonomously during a loop; each tool carries a risk rating and can be gated behind a confirmation step.
Build a library of reusable prompt components—instructions, personas, constraints—that enforce consistency across all evaluations and conversations.
Upload labeled and unlabeled evaluation sets organized into versioned collections. Use them as ground truth for repeatable evaluation scoring.
Run structured prompt evaluations across model variants and configurations. AI agents automatically score and compare outputs against your defined quality criteria.
AI can generate an enormous volume of responses at remarkable speed. Some of it is genuinely valuable. Some is too generic to act on. And some is simply wrong. The problem: most organizations have no systematic way to tell which is which. Moving from a 57% error rate to a 53% error rate is not a business outcome — it's statistical noise. The errors that compound at enterprise scale fall into two types with very different consequences.
At scale, a high false positive rate means investigators and compliance teams spend most of their time chasing red herrings. Alarm fatigue sets in, trust erodes, and the cost of human review balloons.
False negatives are often less visible — nobody sees what wasn't flagged — but they carry the real business risk: regulatory exposure, financial loss, missed compliance violations.
These two error types are captured in a confusion matrix — a structured breakdown of AI decisions against known correct answers. From it, four critical metrics emerge that turn AI quality from a guessing game into a managed discipline.
Enterprise AI data processing workflows — call classification, document analysis, scorecard generation — are brought under quality control through three interlocking objects.
Defines the expected output schema — the exact fields and data types AI must return for every processed object. A call analysis asset specifies category, speaker identification, sentiment, action items, and summary structure. A scorecard specifies evaluation dimensions and scoring rubric.
AI Assets enforce uniformity: every AI run produces the same shape of data, versioned and auditable. When you update a prompt, you bump the version — the old one stays intact for comparison.
A versioned collection of processed objects — call transcripts, emails, documents — used as evaluation input. An unlabeled dataset contains raw objects only. A labeled dataset additionally contains verified ground truth: the correct AI output a subject matter expert expects for each record.
Labeled datasets are the measurement baseline. Without them, you cannot know whether a prompt change improved things or just shifted the noise floor. FoxAdmin uses AI to accelerate ground truth creation — helping teams annotate at a pace that would be impractical manually.
Applies an AI asset version to a dataset and captures every AI output in a structured, comparable table. After improving a prompt, swapping a model, or adding context, you run a new evaluation to measure whether the change actually moved the needle.
Evaluations produce per-record outputs that can be scored against ground truth. An AI agent then analyses discrepancies — distinguishing signal from noise, flagging systematic failure patterns, and recommending targeted prompt corrections.
Organize documents into versioned collections with full metadata. Track lineage from raw upload through chunking, embedding, and indexing.
Control which documents are visible to which agents and users. Apply access policies at the collection level before any retrieval occurs.
Configure chunking strategy, embedding models, and retrieval parameters per collection. Apply reranking rules to boost precision and reduce hallucination in grounded responses.
Every AI model is trained on data that's one to two years old — at best. That data reflects yesterday's regulations, yesterday's policies, and yesterday's market conditions. Laws change. Policies update. Your business evolves. But the model doesn't know any of that unless you tell it. The most sophisticated AI architecture in the world cannot compensate for poor, stale, or absent organizational knowledge.
The body of knowledge that defines how your business should operate. Internal policies, procedures, compliance requirements, approved scripts, decision frameworks — plus external reference material including industry regulations, government guidance, and legal standards your business is obligated to follow.
The record of what has actually happened. Customer interactions, case histories, past decisions, outcomes, exceptions, escalations — the institutional memory that context-dependent decisions depend on. A compliance workflow that can't reference a customer's prior history isn't doing compliance.
Loads knowledge directly into the AI's active context window before it answers — like handing it a pre-assembled briefing document. Fast, reliable, and highly consistent.
Searches your knowledge base dynamically at query time, retrieves the most relevant pieces, and passes them alongside the question — like giving AI access to a searchable library rather than a pre-read briefing.
A query is embedded, matched against a vector database, and the top results are passed to the model. Works for small knowledge bases with well-formed queries.
Retrieve a broad candidate set (50–100 chunks), then apply re-ranking to select the 5–10 most relevant. Incorporates hybrid search, metadata filtering, recency boosting, and cross-encoder models.
Progressive filtering at scale: search 1M chunks → retrieve top 500 → filter to 100 → re-rank to 20 → send best 5–10 to the model. Manages token costs and latency while preserving recall.
Augments vector retrieval with a knowledge graph that maps relationships between entities — people, companies, products, events. Instead of retrieving isolated chunks, Graph RAG traverses connections: a query about a customer can surface related contracts, contacts, and support history in a single hop.
Retrieval as iterative reasoning. The AI retrieves, evaluates sufficiency, and if needed reformulates the query and retrieves again — across multiple sources. When asked "why did our customer churn increase?" it independently pulls churn data, support tickets, and call transcripts before answering.
Even when final outputs look reasonable, the retrieval layer may be silently underperforming — retrieving marginally relevant chunks, missing key documents, or passing redundant context that dilutes reasoning. Without visibility into retrieval itself, quality problems are invisible until they're already affecting outcomes.
Automatically detect and redact sensitive personal data in prompts and retrieved context before it reaches any model. Configurable entity types and redaction strategies.
Detect and neutralize prompt injection attempts embedded in documents or user input. Configurable sensitivity thresholds with audit logging of blocked requests.
All conversation history, documents, and experiment data is scoped per tenant. Configure storage backends and enforce hard boundaries between organizational units.
If your organization hasn't deployed a secure, governed AI environment, employees will build their own. Many already have — with the best intentions. This is shadow AI: the quiet proliferation of unauthorized tools operating entirely outside IT governance, security policy, and compliance oversight. The risks don't require a breach to materialize. They accumulate silently in every unmonitored conversation.
FoxAdmin closes every gap with layered, configurable controls — applied before data reaches a model, during agent execution, and at the governance layer that regulators will eventually audit.
Guardrail against AI hallucination due to reaching max ReAct iterations or max tool call limit. Forces the agent to declare its execution limit explicitly rather than fabricating a completion.
Protects against direct injection attacks from user input — ensures users cannot override system behavior, bypass policies, redefine the agent's role, or manipulate tool execution through malicious instructions.
Protects against indirect injection attacks originating from retrieved documents, attachments, emails, web pages, or vector databases. Retrieved content is treated as untrusted data, not executable instructions.