Tool Catalog
Typed, versioned, discoverable. Every tool carries a schema, a contract, and a policy binding.
Reasoning Is the Start. Action Is the Product. The Layer Between Them Is Where Enterprise Agents Live or Fail.
A language model that produces fluent answers is interesting. A language model that opens a ticket, updates a customer record, posts a journal entry, or fires a call against a production system is operational. The distance between those two things is not a better prompt — it is a full engineering discipline.
Tool-Use & API-Connected Agents is the layer where reasoning meets consequence. It is where an agent's decision to "do X" becomes an authenticated call, against a versioned contract, inside a governed permission envelope, with an observable trail. Everything downstream of the model — from a CRM write to a treasury payment instruction — depends on this layer being right.
Most agent demos stop short of it. Entiovi's work starts here.
Inside a real enterprise, the "tools" an agent has to connect to are not abstract. They are:
Each comes with its own authentication model, rate limits, quirks, schema drift, and failure signature. An enterprise agent is not a model with "function calling enabled." It is a model embedded inside a tool-use runtime that understands those realities.
Between "the model decided to call a tool" and "the system accepted the call" sits a stack we design explicitly.
Typed, versioned, discoverable. Every tool carries a schema, a contract, and a policy binding.
The model's proposed arguments are parsed, coerced, and validated before any wire call.
Scoped credentials, on-behalf-of flows, short-lived tokens, role-based access.
Pre-call checks against business policy, risk rules, and allow-lists.
Retries, backoff, timeouts, circuit breakers, idempotency keys.
Outputs shaped into a schema the model can reason about, without leaking raw payloads.
Full call trace: actor, policy version, arguments, outcome, latency, cost.
We build this stack once per platform and reuse it across every agent that needs to act.
Most production tool-use failures do not come from the model misunderstanding the user. They come from the model handing the runtime a shape the runtime cannot act on. Our approach to function calling is deliberately engineered.
This is the difference between an agent that reliably executes and an agent that looks good in a screenshot.
Every tool an agent can call defines a blast radius. A "read order" tool is low-radius. A "submit payment" tool is not. Our permission design follows three principles.
Agents run under scoped service accounts with only the permissions the task requires.
Every tool is tagged by impact class — read, write, destructive, financial, regulatory — and each class has its own policy gates.
When an agent acts on behalf of a user, the user's identity is carried through to the downstream system. Audit logs show who asked, not just which agent executed.
High-radius actions are not merely "confirmed by a human." They are structurally constrained: amount limits, counterparty allow-lists, time windows, dual-control flags, and regulatory checks are enforced in the runtime, not in the prompt.
Production tool-use is a reliability engineering problem. The model is one source of error; the tool surface is a bigger one. Our reliability patterns:
The goal is a tool-use layer whose failure modes are understood, bounded, and recoverable — not silenced by a retry loop.
Any agent that can change state in an enterprise system must produce an auditable, defensible trail. Every tool call captured by an Entiovi runtime carries:
The agent, the user on whose behalf it ran, and the policy version in force.
Tool name, version, arguments (redacted where needed), timestamp, correlation ID.
Which gate allowed it, which policy applied, which exceptions were granted.
Response status, normalised payload, downstream system IDs, compensations triggered.
Credential source, token expiry, network path, data classification.
This is not a log stream. It is a structured audit surface, queryable by compliance, security, and operations — each in their own view.
These are patterns we have productionised across engagements, not reference diagrams.
In every case, the agent is not producing a recommendation for a human to implement. It is doing the work, in the system, under policy.
Catalogue of target systems, contracts, auth models, rate limits, data classifications.
Tool taxonomy, impact classes, policy map, credential strategy, failure modes.
Typed tool clients, policy gates, tool runtime, audit surface.
Load testing, schema-drift testing, failure injection, security review.
Phased exposure per impact class, starting with read-only and advancing under measured confidence.
Continuous contract validation, drift detection, access reviews, policy refresh.