Pyrana Overview

PYRANA® Overview

Agentic applications, not agents.

October 2026

Introducing PYRANA

Production-grade AI applications, not chat boxes. Rapidly built around your processes, informed by your knowledge, controlled by the platform.

Outcome driven

Applications, not a text box

Chat makes every user responsible for knowing what to ask. Agentic applications are shaped around the task and bring the work to the person, so results depend on the process, not on who is prompting.

Compounding Scale

Every application accelerates the next

Take one use case from pilot to production, extend it across teams and sites, then add the next. The foundation and the business knowledge are shared, so every application builds on the last.

Risk Mitigating

Guardrails outside the model

Permissions, approvals, and limits are enforced by the platform, not by a prompt. Agents inherit the access and authority of the people they work for, and every action has an audit-ready history.

Introducing PYRANA

Powerful, AI-powered enterprise applications you can trust

THREE KINDS OF SOFTWARE, AND WHERE PYRANA SITS

Systems of record

Software that keeps track. People do all the work.

Copilots and chat tools

Software that helps one person at a time. The result depends on that person's skill and on what they thought to ask.

Agentic applications: PYRANA

Software that carries the work. Agents do the analysis inside an application built around the process; people decide inside it.

HOW IT WORKS: EVERY APPLICATION IS CONFIGURATION ON ONE PLATFORM

Month-end close

Contract review

Order exceptions

+ the next one

SHARED ONCE, REUSED BY EVERY APPLICATION

Business knowledge

System connections

Identity and permissions

Approval rules

Audit record

PYRANA HARNESS: ONE RUNTIME FOR EVERY AGENT RUN

Durable

Saves each step. Resumes after a crash or a long approval wait.

Any model

Chosen per application. Your keys, your data, no training.

Governed

Identity, approvals, limits, and the record enforced on every call.

WHAT MAKES IT DIFFERENT

Outcome driven

Applications, not a text box. Shaped around the task, so results depend on the process, not on who is prompting.

Compounding scale

Every application accelerates the next. Foundation and business knowledge are shared, so each one builds on the last.

Risk mitigating

Guardrails outside the model. The platform enforces permissions, approvals, and limits; every action has an audit-ready history.

The hard part is already built. Each application is configuration on top: the process, the pages, and the knowledge, not the plumbing.

Why Agentic AI, and Why Now

When software does the work, the economics of every function change

Systems of record

Software that keeps track. People do all the work.

Generative AI and copilots

Software that helps. People still do the work.

Agentic applications

Software that carries the work, with people deciding inside it.

WHAT IT CHANGES FOR THE BUSINESS

Operating leverage

Volume grows without headcount growing in step.

Speed

Cycle times move from days to minutes.

Consistency

Every decision made with the right knowledge, every time.

Resilience

Institutional knowledge stays when people leave.

10 to 25%

EBITA gains from scaling agentic and single-task AI

Bain, 2025

30 to 50%

acceleration in business processes

BCG, 2025

70 to 80%

lower cost per transaction in labor-heavy processes

McKinsey, 2025

Why now: the models are ready. What separates the winners is how safely, and how fast, they put them to work inside the business. Early movers are already compounding the advantage.

What an Agentic Application Is

People and agents share the same pages, data, and knowledge, under one identity and one record

YOUR BUSINESS people, systems, knowledge, customers

THE APPLICATION the job to be done and the rules around it

who may do what

what needs approval

which limits apply

the record of every action

THE AGENT a participant, not the application

Reads the documents and systems, works out what to do, acts within the limits, asks a person when the policy says so, and cites what it used.

It never sets its own rules. It holds only the access the application grants, including its own credentials to outside systems, and every action is on the record.

What agents do

Gather the facts from documents and systems

Analyze and recommend

Act within the limits you set

Ask a person when the rules say so

Remember, and improve with use

What your people do

Set the rules and the limits

Approve what matters

Handle exceptions and judgment calls

Spend time on customers, strategy, and relationships

Everyone, human or agent, passes the same gate: same identity, same authorization, same record.

Working in the background

Agents run the process end to end and bring people only the exceptions.

Example: the monthly financial review across every business unit, delivered in minutes instead of days.

Working in the application's pages

People work in the application's own pages. The copilot sits beside the page for questions about what is on screen and for personal pages; it is not the primary interface.

Example: a buyer completing a procurement request on a page, with the policy applied as they go.

Meet PYRANA

Build, run, and govern agentic applications on one platform, private tenant or your own infrastructure

App pages

Copilot

APIs and integrations

Other agents via MCP

Schedules and events

THE GATE every call, human or agent, passes eight steps before anything runs: identity, App version, authorization, approval policy. Fails closed. The record is written before dispatch.

AGENTIC APPLICATIONS

One App configuration each: surfaces, targets, tool sources, resources, access, knowledge. Examples below.

Financial close and review

Collections

Procurement

Marketing content

IT project management

Pyrana Harness

Runtime and orchestration: planning, tool use, subagents, durable workflows, approval policies, checkpoint and resume. Five lanes: direct, agent, workflow, batch, event. A model assigned per agent.

cortIQ context engine

Documents and tacit knowledge become typed, classified, evidence-backed Context Units in a knowledge graph. Agents retrieve and cite them; every run accounts for what it used.

ENTERPRISE FOUNDATION SaaS on a dedicated private tenant, or your own infrastructure · SSO and authorization in layers · tenant isolation · one execution record per call · your data never trains external models · model per agent

WHAT MAKES IT DIFFERENT

One authorization model for people and agents

Identity, authorization, approvals, and audit enforced once, on every door. Each agent carries its own identity and credentials, inside and out.

Context engineering, built in

A shared context engine with source-level provenance. Knowledge compounds across apps with use.

Applications, not agents

The basics are already built and live in the platform. Teams build the process, the pages, and the knowledge, not the plumbing.

The platform is built once. Each application is configuration, and value arrives in weeks.

What a Team Sees

Elegant, user-friendly application pages. The copilot sits beside the page; it is not the primary interface.

ILLUSTRATIVE the monthly run published the pack; the controller asks the copilot about one account and revises the summary in place. Pyrana Capital is a synthetic portfolio.

WHAT THE PAGE SHOWS

Pages people work in

Entities, KPIs, an executive summary, and a chart on one App page. Not a chat window.

Copilot beside the page

Auxiliary: questions about what is on screen, and personal pages. The page stays primary.

Every figure cites its unit

Numbers carry Context Unit references. The run that produced them is on the record.

Why PYRANA

Because the hard part is already built

A framework

You get an agent. Security, authorization, and storage are still yours to build, and to maintain as agents multiply.

Vertical applications

One finished product per job, each with its own identity, permissions, and data. Nothing between them. CIOs do not want twelve of them.

A platform: PYRANA

Identity, authorization, storage, orchestration, approvals, and audit built once. Every application composes on it as configuration, with people inside the loop.

SPEED TO VALUE

The basics are already built

Identity, authorization, audit, storage, orchestration, approvals, and context live in the platform. A team builds the process, pages, and knowledge.

About a week

to stand up the App configuration, with clean data available. The frontend experience is the main effort.

About six weeks

from decision to a first App in production, with real identities and approvals. Confirmed per engagement.

Less each time

Every App after the first inherits the identity model, access profiles, approval lanes, audit record, and Context Units.

Built and delivered by experienced operators, in production with enterprise clients in regulated industries today.

Pilots Are Easy. Production Is the Problem.

Frameworks and vertical applications both leave the hard part undone

VERTICAL APPLICATIONS

One vertical AI product per function. Each arrives with its own identity model, permission semantics, audit format, and storage.

Finance AI

own identity

own permissions

own audit log

own storage

Procurement AI

own identity

own permissions

own audit log

own storage

IT Ops AI

own identity

own permissions

own audit log

own storage

Context stays trapped in each silo. Integration glue is rebuilt for every pair of systems and governed by none of them.

A FRAMEWORK

Agent frameworks and cloud agent services solve the visible part: a model, a prompt, and some tools.

The agent

what the demo shows

below the waterline

Fine-grained authorization

Tenant isolation

Durable execution

Human approval policy

Idempotent external effects

Audit, secrets, versioning

The hard part sits underneath every application. It is expensive, security critical, and usually bolted on after the first incident.

40%+

of agentic AI projects will be canceled by the end of 2027, citing cost, unclear value, and inadequate risk controls

Gartner, 2025

50%

of enterprise AI agents operate in isolation: no shared data, no coordination, no handoffs between them

Salesforce, 2026

21%

of organizations have a mature governance model for agentic AI

Deloitte, April 2026

The third way: a platform that does the hard part once. Applications as configuration, agents where they help, people deciding inside.

PYRANA: The Agentic Application Platform

Build governed enterprise applications where agents are participants, not the product

App pages

Copilot

API

MCP for agents

Schedules and events

THE GATE, EVERY DOOR who is calling, which App version, is it allowed, does policy require a person. Eight steps, fail closed, the record written before anything runs.

AGENTIC APP

one App configuration, compiled into an immutable version, activated with one pointer, rolled back the same way

Surfaces

the pages people work in

Targets

operations, agents, workflows

Resources

versioned, attributed outputs

Access

profiles, scope entitlements, approval lanes

Extensions

client-owned services, same cluster

PYRANA HARNESS

runtime, five lanes

Agent

adaptive

Workflow

durable

Batch

at scale

Direct · Event

no model

cortIQ context engine documents and tacit knowledge as typed, classified, evidence-backed Context Units. Retrieved, injected, cited, per run. Shared by every App.

Your ecosystem tools, data sources, transactional systems, and your own code, connected through MCP, APIs, connectors, and extensions, under governed operations.

ENTERPRISE FOUNDATION SSO identity (Entra ID, OAuth) · tenant isolation with row-level security · authorization in layers (OpenFGA) · per-agent identity and credentials · SOC 2 aligned audit and telemetry · SaaS on a dedicated private tenant, or your own infrastructure

One identity.

One authorization model.

One audit trail.

One shared context.

Faster

The basics are already built. New applications are configuration. Value in weeks.

Safer

Governance is built once and enforced on every call, human or agent.

Compounding

Context, outputs, and learning are shared across Apps.

SaaS on a dedicated private tenant, or deployed into your infrastructure. In production with enterprise clients today.

Agentic Apps: One Application, Many Doors

One set of rules for every person, agent, workflow, and system in the application

Manager drags a card

Colleague asks copilot

System calls the API

Agent calls MCP tool

Nightly workflow runs

ONE OPERATION project.stage.transition defined once, called from every door, by person or agent

THE GATE resolves the caller, App version, profile, scope, and exact resources; writes the record and claims idempotency before dispatch. Same eight steps whichever door; each caller keeps its real identity.

INSIDE THE APP

one App configuration, six sections, one active version

Surfaces the pages people work in

Targets operations, agents, workflows, from every door

Tool sources REST, ODBC, MCP, warehouses, drives

Resources published outputs, versioned

Access profiles, scope entitlements, approval lanes

Knowledge standing sets and context search

Author config

Compile, pin digests

Immutable version

Activate: one pointer

Roll back

Why not assemble it from components?

COMPONENT BUILD

AGENTIC APP

Permissions

Every door grows its own lock; policy drifts.

One target, one gate, every door.

Agent authority

Agents call any tool they can see.

Agents hold their own identity, inside and out.

Outputs

Trapped in the run or thread that made them.

Versioned, citable resources with provenance.

Your domain code

Forks the platform or leaks into shared code.

Runs as a governed extension in your cluster.

Change control

Config edits go live unversioned.

Compile, version, activate, roll back.

Agents are participants in the app, not the app itself.

cortIQ: Company Knowledge, Typed and Cited

Documents and tacit knowledge become Context Units: typed, classified, evidence-backed

Sources

Documents, systems of record, expert input, and past sessions.

Extract

Lenses turn sources into Context Units: atomic claim, verbatim evidence, provenance, hash, knowledge type, classification.

LLM extracts. Code validates.

Knowledge graph

Postgres as truth, Neo4j for relationships, Qdrant for vectors. Sets scope knowledge; rank-0 units are always-on policy.

Persistent knowledge, not chunks

Retrieve

Three modes: knowledge, facts, and search. Standing sets preload; the agent searches mid-run.

Better context beats more context

Agents act

Every unit is accounted for as retrieved, injected in the prompt, or cited in the result. Provenance is a record, not a claim.

retrieved ⊇ injected ⊇ cited

CURATION propose, gate by classification (regulated waits for a person; absolute never auto-changes), apply under compare-and-set. Superseded units stay traceable. A learning agent scores what was retrieved and used and flags units that never help for review.

GOVERNED regulated or absolute knowledge cannot be retired by weak usage signals. Humans review; the engine recommends.

BEYOND RETRIEVAL

Typical RAG or agent memory

cortIQ

Unit of knowledge

Text chunks sized for the embedding model

Atomic claims with evidence, provenance, and a citable hash

Retrieval

Similarity search over one index

Graph, vector, keyword fused and reranked; policy always on

Provenance

A log of documents retrieved

Retrieved, injected, and cited tracked per unit, per run

Improvement

Static index; re-embed to change behavior

Learns from use under governance; protected knowledge stays

Memory

Conversation transcript replay

Session memory over temporal, semantic, and reference links

An Agent Harness Built for Enterprise Work

A proprietary, model-agnostic runtime with durability and human oversight built in

Native tool calling and subagents

Plans, calls tools, delegates bounded work to named subagents, and reads results through scoped views.

Durable execution

Temporal-backed workflows and dynamic DAGs. Suspend, checkpoint, and resume after any interruption.

Human approval as policy

Typed conditions, named approval lanes, frozen invocations, revalidation before dispatch. Never pays twice.

Model agnostic

A model is assigned per agent through provider adapters and never switched mid-run. Bring your own keys.

Does the job, every time

Deterministic checks confirm the required output, structure, and evidence before an agent can finish.

Plugs into your ecosystem

Your tools, data, and transactional systems via MCP, APIs, connectors. Your code as an extension.

Explicit state and virtual file system

Plan and knowledge change only through tools. Path-based access to files and context, no raw credentials.

One causal trace

Admission, model calls, tools, approvals, effects, outputs: one identity. Evaluate and audit any run.

ONE GOVERNED LOOP

Admit

authority

Plan

explicit steps

Act

tools, subagents

Observe

checkpoint

Validate

schema gate

Publish

trace, receipts

Agent

Every agent has its own identity, in the platform and in the systems it touches: one agent may hold SharePoint site A, another only site B. Every child call re-enters admission.

FIVE LANES behind one gate. Direct runs deterministic work with no model; the four below run the rest.

Agent adaptive, with tools

Workflow durable, long running

Batch many entities at once

Event schedules, webhooks

Comparing Agentic Solution Approaches

All three get one agent live. They diverge on every application after it.

APPROACH

WHAT ARRIVES

EVERY APPLICATION AFTER THE FIRST

SECURITY AND AUTHORIZATION

CONTEXT AND KNOWLEDGE

CONTROL AND LOCK-IN

Vertical applications

One product per job: Sierra, Harvey, Jasper, Wonderful

One finished application per job, each with its own identity, permissions, and data.

Another product, another identity model, another audit format.

Each vendor’s own. Nothing between them.

Trapped inside each product. Re-onboarded for every vendor.

Vendor cloud, vendor data model, vendor’s choice of models.

A framework

Agent SDKs and cloud agent stacks: AgentCore, Foundry, Vertex, LangChain

An agent. Security, authorization, and storage are still yours to build.

Rebuilt per agent, or a platform team maintaining what the framework left out.

The enterprise’s to build, and to maintain as agents multiply.

Per-agent memory. Cross-app knowledge is another build.

Full control, full burden. Primitives tied to one cloud.

A platform

PYRANA

The foundation, built once: identity, authorization, storage, orchestration, audit.

One App configuration. Same identity, authorization, audit, and context. Basics never redone.

One model, decided on the backend in layers, shared by every application.

cortIQ shared and cited across every app, learning from use.

Your tenant or your infrastructure, your data, your tools, any supported model.

Agent control planes (Agent 365, Agent Fabric, AI Control Tower) govern agents under any approach and build no applications.

The foundational engineering is done once, in the platform. That is what value in weeks depends on.

Questions Buyers Ask

The short answers. The site carries the long ones.

Hosting

SaaS on a dedicated private tenant, or deployment into your own infrastructure for large enterprises. Temporal workers provide durability and horizontal scaling in both.

Portability and lock-in

One YAML configuration per application; agent instructions in plain language; declarative workflows; Context Units are your data. Nothing is opaque.

Data layer

No copy of your data is required. Agents query live through tool sources: REST, ODBC, MCP, Databricks, Snowflake, Drive, OneDrive. Cleansing runs in a code interpreter or an ETL layer upstream.

Models

A model is assigned per agent through provider adapters and never switched mid-run. Bring your own keys.

Evals

Every run’s traces, tokens, and tool errors are captured as JSON today. Deterministic checks first, then LLM-as-judge.

API

A full administrative API: push and pull configuration, start runs, read runs and traces. Any agent can work with an App over its MCP server, as the user.

Time to build

About a week for the App configuration with clean data; about six weeks from decision to a first App in production, confirmed per engagement.

Security review: questionnaires answered with mechanisms and documents, on request.