Orchestration
Coordinate specialist agents, tool calls and human decisions across a process.
The future of enterprise is agentified
The Agent OS your enterprise can trust.
“Agents are easy to demo, hard to trust with real work.”
Build enterprise systems where AI agents, business tools and people work together — with shared context, controlled actions and a record of what happened. Enter through FP&A, then expand into the business behind the forecast.
YOUR FP&A FORECAST
→
ONE AGENT SYSTEM
→
FULLY AGENTIFIED ENTERPRISE
The enterprise entry · built on Olbrain
Enter through FP&A. Expand into the business that drives the numbers.
The first system brings specialised agents, a calculation engine and finance professionals together around the forecast. Agents prepare and explain the work; finance reviews the evidence and decides what to accept. From there, the intended path follows the drivers behind the numbers into the workflows that run the business.
FP&A: follow the number, keep the decision.
Inputs
Sales volume
1,000 units
Source: illustrative planning input · sales owner
An input carries its source reference, owner and method. Finance can review where a number came from.
Review queue
Proposed selling price
₹120 / unit
Proposal · awaiting finance review
The agent extracts a candidate value and explains its basis. A proposal does not change the forecast.
Finance review
Selling price accepted
₹120 / unit
Accepted by finance · illustrative decision
Finance reviews the evidence and accepts the input. Only accepted changes apply.
Calculation engine
Revenue = volume × price
₹120,000
1,000 × ₹120 · approved formula
The financial engine computes the numbers from confirmed inputs and approved formulas. Agents help with the work around the calculation.
Run library
Illustrative run A
₹120,000 revenue
Fixed snapshot · accepted inputs and formulas
A run preserves the input and calculation state. Later changes create another run; they do not rewrite this snapshot.
Management review
What if volume rises 10%?
₹132,000 revenue
Scenario: 1,100 × ₹120 · run A stays fixed
Explore a scenario and explain its movement: revenue rises ₹12,000 with price held constant. Reporting uses a selected run and templates with mapping and review before release.
Step 1 of 6
Illustration of the workflow, not a customer screenshot or measured result. Phase 1 is built and awaiting validation and demonstration.
The staged expansion path
Follow the forecast into the business.
Each stage extends the work on the same Agent OS, with its own validation, permissions and human approvals. This is the intended path toward an agentified enterprise; expansion depends on demonstrated value and enterprise decisions.
Stage 01 · entry
The FP&A forecast
Reproduce the existing process and validate the numbers. Phase 1 is built and awaiting validation and demonstration.
Stage 02 · planned
The drivers behind it
Extend forecast preparation into sales, inventory and raw materials. Agents for 24 business drivers are planned.
Stage 03 · intended expansion
The operating workflows
Build systems for the work behind those drivers. Forecast support and operational execution have separate permissions, validation and approval needs.
Stage 04 · long-term direction
An agentified enterprise
Gradually connect approved systems across functions, with the enterprise controlling decisions and the Agent OS carrying the shared foundation.
The reusable operating layer
One system first. A foundation for the next.
The forecast is the entry point. As further systems are validated and approved, the same Agent OS connects agents, tools, data and human decisions across business functions.
Tools & calculations
Connect deterministic engines and business tools to the work agents prepare.
Data connections
Bring the required data into a system with source references and integration boundaries.
Permissions & approvals
Define what may happen and where a person must review or accept a change.
Shared state & records
Carry workflow context and preserve records that support review and attribution.
Human workspaces
Give people a place to inspect evidence, compare outputs and decide what happens next.
The same foundation supports lending operations and research. Their customer stages differ: lending agents are in UAT or requirements, with the programme on hold; the first research agent is tested, with paid conversion still open. Reuse supports further systems, while integrations and domain rules still need validation.
Explore the Agent OS →The problem
Agentification is a climb, not an install.
A useful suggestion is a starting point. Recurring enterprise work also needs connected data, reliable tools, permissions and people who can review and approve decisions. The right level of autonomy depends on the job and its oversight needs.
Olbrain’s illustrative autonomy ladderMatch authority to the work
L0Assist · help a person complete work
L1Suggest · propose options for review
L2Execute · complete an authorised bounded task
L3Orchestrate · coordinate agents, tools and human decisions
L4Optimise · evaluate improvements within approved limits
L5Self-evolve · adapt under an explicitly governed mandate
A framework for discussing capability and oversight, not enterprise adoption statistics.
Build, run and govern each system on a shared operating layer. Reusable capabilities can support the next objective; customer-specific tools and workflows still require engineering and validation.
Enterprises let agents talk long before they let them act, because a talking agent’s mistake is recoverable. Adoption follows containment. Climbing the ladder means making action as checkable as conversation.
The starting point
Start with the forecast.
Olbrain is one operating system for the whole climb — built, run and governed on the same foundation, so every rung builds on the last. An enterprise does not need to design its final agent architecture on day one. We start where operating decisions start: the FP&A forecast. Its inputs sit with different owners, it has to be reconciled against its sources, and every movement has to be explained to management. The first agent system reproduces your finance team’s forecast — on its own methods, assumptions and data — before it is trusted with anything more. What that first system recovers, connects and proves stays on the platform for the next.
Agent system
A single-agent system or multi-agent system designed to achieve a defined business objective, together with the data, tools, workflows and governance required to operate it.
Built on Olbrain
What our clients have already built.
6
agent FP&A system
A listed manufacturer’s six-agent FP&A system is built to reproduce the finance team’s existing process and financial model. Phase 1 awaits validation and demonstration; management approval and paid production remain open.
4
agents in UAT
A regulated lending NBFC: four agents co-built and in UAT, one at business-requirements stage, five more identified. The programme is on hold; acceptance and paid production remain open.
8
more requested
A research consultancy built and tested its first agent and identified eight additions. Continued use, first recurring payment and paid expansion remain to be established.
Ten recorded engagements, all by referral. Pre-revenue · ₹15L monthly burn · about ten people.
See all 12 use cases built on Olbrain
01
FP&A and P&L projections
Bring operating inputs together, reproduce rolling forecasts, explain what changed, and answer management’s sensitivity and what-if questions.
02
Lending and regulatory operations
TDS, CGTMSE, direct assignment and NACH processes—with rules, approvals, exceptions and traceable execution.
03
Customer support
Understand a customer’s question, use the relevant business and order context, resolve it, or hand it to a person.
04
Sales and recommendations
Discover customer needs, compare products, make recommendations, answer objections and move the customer toward purchase.
05
Shopify store management
Work across products, inventory, orders, discounts, fulfilment and refunds—with merchant approval before changes are made.
06
Research reports
Gather evidence from internal knowledge and external sources, reason over it, and produce structured, sourced reports.
07
Market and tender monitoring
Monitor selected sources, identify relevant opportunities, extract requirements and notify the right team.
08
Technical hiring evaluation
Conduct structured candidate conversations and evaluate the evidence against the organisation’s hiring criteria.
09
Candidate matching
Compare roles and candidates, surface the strongest matches, and show the evidence behind each recommendation.
10
Lead generation and outreach
Research prospects, qualify opportunities, conduct personalised outreach and route interested leads to the team.
11
Voice calling
Make and receive structured calls, capture the outcome, follow the business process and hand over when required.
12
Document processing and verification
Read statements, contracts and forms; extract information; apply checks; and route exceptions for human review.
How Olbrain works
From business objective to a running agent system.
Your engineers can build on Olbrain themselves, our forward-deployed engineers (FDEs) can build with you, or the teams can co-build. The same Agent OS supports all three motions.
01
Define the objective
Begin with the business result, current process, source systems, decision criteria and human approval points.
02
Build and test
Olbrain maps the work and assembles the agent system—its instructions, knowledge, tools, workflows, triggers and controls.
03
Connect and publish
Connect the required data and tools, validate outputs against the current process, and review permissions and approvals. The enterprise decides when the system is ready to publish.
04
Run and govern
The live system runs on Olbrain with policy enforcement, observability, lifecycle management and an attributable record of its actions.
05
Expand
Once the first system proves value, adjacent functions can be agentified on the same operating layer.
Trust infrastructure
Agents climb only as far as the trust infrastructure lets them.
Every control an enterprise runs — permissions, certification, audit, accountability — attaches to an actor. Most agents today are processes re-created each run: there is no continuous “who” to govern. Self-identity creates the actor. Olbrain’s trust infrastructure — self-identity plus a signed, append-only record — supports attribution of recorded actions to an identity. Full cryptographic enforcement of continuity and exclusivity remains in development. Access control decides what an agent can touch. Olbrain establishes who it is.
01
Auditable identity
Agent actions are bound to an identity designed around coherence, narrative continuity and exclusivity. Self-identity and the signed, append-only record are live. Cryptographic enforcement of exclusivity and narrative continuity is the frontier being built.
02
Attributable actions
Recorded actions carry signed receipts in an append-only record, supporting attribution to the identity that acted. Complete capture across every action path must be evaluated for each deployment.
03
Protected data
PII is detected and tokenized before a language-model call, with per-tenant isolation and enterprise controls around access and deployment.
Olbrain supplies the trust infrastructure. The enterprise keeps the accountability.
Autonomy arrives not when agents get smarter, but when the trust layer catches up.
Before the work is handed over
Accountability asks five questions of every agent.
Building an agent is the easy part. Whether an enterprise can hand it real work — and still answer for the outcome — is decided by five questions. Each deployment must establish its answers through validation, oversight and the available record.
01
How far up the autonomy ladder does it actually run?
Can it run multistep work end-to-end, with humans only on the exceptions — or does a person still drive every step? Real operating value lives high on the ladder. Most agents today stop well below it.
02
Is every action on the record?
A signed, append-only, checkable audit trail — or a log you take on faith? You can only stand behind what you can verify.
03
Does each agent have a continuous self?
A provable, continuous “who” that governance, audit and compliance attach to — or a fresh process every run, with no actor to hold answerable?
04
When it gets something wrong, can you trace it and fix it?
Can you see exactly what the agent did and why, correct it, and have the learning stay with that same agent — not vanish into the next anonymous run?
05
Who stays accountable?
The enterprise does. Always. The agent is answerable for its actions — traceable, correctable, on the record — so that the people accountable can discharge that duty with confidence. The line never blurs.
Trust infrastructure is neither the cheapest way to build an agent nor the fastest. It is what lets an enterprise hand one real work — and still answer for it.
Trust infrastructure, written down
The architecture, written down.
Trust infrastructure rests on self-identity — coherence, narrative continuity and exclusivity. The ideas are set out in full in our whitepapers, including the formal, mathematical foundations of the CNE-Protocol.
Whitepaper
Formal Foundations of the CNE-Protocol
A mathematical treatment of agent identity: why memory and coherence alone cannot individuate an agent — and what does.
Read the paper →Whitepaper
The CNE-Protocol
The Agency Protocol: identity as one coherent, continuous, exclusive narrative per Core Objective Function.
Read the paper →Whitepaper
Coherence: The C in CNE
What it means for an agent to stay consistent with its own past reasoning — and what an auditor can do with the record.
Read the paper →Build and test before you publish.
Enterprises can experiment, build and validate their agent systems before making a production commitment.
Know when each charge starts. The organisation platform fee starts at approval; indexed knowledge-base storage starts at upload. Each published Agent System receives 30 days free of Agent, orchestrator and usage charges; those charges begin on day 31 unless cancelled. Approved FDE services are charged separately. See pricing and billing terms →
The first conversation
Bring your current forecast.
Bring the model and its source files. We will show you how it becomes a governed agent system on Olbrain, reproduced on your own numbers — or bring any other business problem you would agentify first.