Fontana agents support analysis, drafting, explanation, and setup. Human approval stays at the control points, while production workflows run through approved rules, ownership, lineage, and replay.
LLM Catalogue
Fontana is AI agnostic: route any LLM through any approved gateway that fits your compliance, residency, and data-handling requirements, without locking workflows to one vendor or model family.
AI should not weaken control. Fontana separates AI assistance from production authority. Analysis and drafting can run automatically, while production-impacting actions require human approval, everything has full audit evidence.
Analyse files, schemas, and source outputs without approval
Infer fields, mappings, and validation checks for review
Draft operating specifications and mapping proposals
Explain breaks, exceptions, and supporting evidence
Propose workflow configuration before it is applied
Summarise evidence packs for reviewers
Change production rules or workflow boundaries
Approve exceptions, outputs, or control changes
Execute operational steps in production
Bypass prompt, output, routing, version, and decision logs
Override named owners, approval gates, or control policies
Act outside defined permissions and data-class boundaries
Fontana's execution boundary remains deterministic. If a workflow, data class, client, jurisdiction, or policy requires no AI assistance, the approved rules, validations, approvals, lineage, replay, and audit evidence continue to operate without model involvement.
Assistive analysis and drafting where policy allows.
Human-owned control decides what is accepted.
Rules execute deterministically with the same evidence requirements.
Technology and control teams need to know what was asked, which data was referenced, which model or agent was used, what came back, and what humans decided afterwards.
Fontana gives operations teams full control with complete visibility over workflow activity, approvals, changes, and outcomes. Every process can be audited, traced, and replayed when needed.
Own approvals, exception handling, rule changes, and production decisions. AI helps prepare the work; operators decide what becomes accepted process.
Audit prompts, outputs, routes, model usage, versions, permissions, lineage, and replay context without treating the AI layer as a black box.
Route frontier, open, and domain-specific models through OpenRouter, direct APIs, Bedrock, and OpenAI-compatible gateways, or connect a private deployment your architecture team hosts inside the perimeter.
Connect gateways with your firm's own API keys and credentials, stored separately from model routing and never passed through the thread.
Fine-grained configuration of all agents, from system prompts and reasoning level to context pool, temperature, cost guards, and more.
300+ Models
Frontier, open, and domain-specific models from major providers
12+ Gateways
OpenRouter, direct APIs, Bedrock, and OpenAI-compatible routes
Fontana Platform
Governed routing, policy, logging, and workflow boundaries
Create agents for analysis, workflow setup, and review. Use skills, sub-agent delegation, and handoff to keep threads focused and token use efficient.
Use SubAgent Delegation to isolate deep work in child threads while the parent keeps context lean.
Use Agent Handoff to transfer the root thread to a specialist agent when the task surface changes.
Use Agent Interop with A2A, ACP, ANP, OFP, and other standards to communicate with external agents.
Organise operational knowledge (standards, firm rules, mappings, prior decisions, and workflow logic) into namespaces your teams control. Agents assemble thread context from what you have validated, not ad-hoc uploads.
Build a knowledge graph to model your firm's standards, rules, and decisions.
Use Agent Skills to add tools and context briefly for specific tasks without bloating every turn.
Allow agents to self-improve using auditable, permanent memory that persists across threads.
Allow AI to run complex queries on your data to search, group, filter, aggregate, and more, without pushing bulk datasets through the model.
Agents can query data directly using SQL, TypeScript, Python, and more; results are returned to the thread without bulk data entering the model context.
Use MCP to access external data sources, perform financial data lookups, or communicate directly with your firm's tooling.
Combine Knowledge Graph context with live queries to produce findings reviewers can trust. Attach query definitions, cite approved sources, and export review-ready evidence, not unsupported narrative.
Render charts and visualisations inline in the thread so reviewers see findings alongside the query proof that produced them.
Generate Markdown, Excel, Word, and PDF documents, complete with citations, and export-ready structure on the audit trail.
Generate structured questionnaires, clarification checks, and approval forms mid-analysis or mid-workflow build. Collect human input as steps in the thread, not side-channel email or ad-hoc chat, so decisions stay on the audit trail.
Surface workflow suggestions in-thread so reviewers can accept or reject proposed changes without leaving the audit trail.
Collect questionnaires, clarifications, and approvals as structured forms embedded directly in the agent thread.
