Given an outcome, it plans the steps to reach it.
Reads and writes to real systems rather than describing them.
Carries context across steps, sessions and cases.
Escalates at defined checkpoints instead of guessing.
Every step, tool call and decision is traced and retained.
What is agentic AI?
Produces an output. A human acts on it.
Takes the action itself.
Agentic AI is software that pursues a goal by planning its own steps, using tools to read from and write to real systems, and deciding when to act and when to hand back to a person.
The difference from generative AI is authority. A generative application produces an output that a human then acts on. An agent takes the action itself, which is why the engineering question stops being "is the answer good" and becomes "what is this allowed to do, under whose permissions, and what happens when it gets it wrong."
That shift is the entire reason enterprise agentic AI is harder than a pilot. The model is rarely the constraint. Identity, integration, evaluation, oversight and reversibility are.
Four things get called AI. Only one of them acts.
Useful to settle early, because the governance, the cost and the build effort are completely different at each level.
Answers from a fixed script or a single model call. No access to enterprise data.
Retrieves from your own content and answers with citations. Read-only.
Drafts, summarises and pre-fills, then a person reviews and submits.
Plans the steps, calls the systems, completes the work and escalates only the exceptions.
Nine parts of a production enterprise agent.
Every one of these ships with the IX AI Foundry, which is why an engagement starts at your workflow rather than at the framework.
Decomposes a goal into steps, chooses an order, re-plans when a step fails or returns something unexpected.
Foundry: Agentic AI RuntimeThe definitive list of what this agent can call, with schemas, permissions and rate limits attached to each tool.
Foundry: Agentic AI RuntimeShort-term working context and longer-term case memory, so an agent does not restart from zero on every turn.
Foundry: Agentic AI RuntimePermission-aware access to your own documents, records and knowledge, with lineage and citations.
Foundry: Knowledge ServicesRead and write against systems of record, not exports. This is where most agentic projects actually stall.
Foundry: Integration FabricIts own identity and permission scope, inherited from your access model. It cannot reach what its user could not.
Foundry: AI Control PlaneDefined checkpoints where authority returns to a person, with escalation, override and reversal paths.
Foundry: Agentic AI RuntimeGolden sets, trajectory tests and regression gates. We know the accuracy, and we know when it moves.
Foundry: AI Control PlaneEvery step, tool call, retrieved source and decision retained and searchable, for debugging and for audit.
Foundry: AI Control PlaneCustom Agentic AI Development Services for Enterprise Workflows
Named by the shape of the work rather than by the technique. Most delivered solutions combine two of these.
Intake and validation
Unstructured input arrives, the agent extracts what matters, validates it against business rules and reference data, then creates or updates the record in the system that owns it.
Typical workflows- RFQ and quote intake
- Loan and application file assembly
- Claim and warranty submission
- Supplier and customer onboarding
Exception triage and resolution
The agent works the queue nobody has capacity for. It classifies each exception, gathers the context needed to resolve it, resolves what it can and escalates the rest with the evidence attached.
Typical workflows- Reconciliation breaks
- Order and inventory exceptions
- Document condition resolution
- AML and fraud alert triage
Decision preparation
Where the decision must stay with a person, the agent does everything up to it: assembles the file, applies the policy, scores the risk, drafts the recommendation and shows its reasoning.
Typical workflows- Credit and underwriting decisions
- Pricing and discount approval
- Bid / no-bid assessment
- Supplier and vendor risk review
Multi-system orchestration
Several agents, each responsible for one domain, coordinated by an orchestrator that holds the overall goal. Used where one process spans systems that have never talked to each other.
Typical workflows- Quote to order to fulfilment
- Dealer credit, pricing and reimbursement
- Trade compliance and customs
- Cross-system service resolution
Monitoring and intervention
The agent watches a stream of events continuously, detects the conditions that matter, and either acts within its mandate or raises the issue to the person who owns it.
Typical workflows- Supplier and tariff risk monitoring
- Delinquency and collections prevention
- Portfolio and concentration risk
- Asset and quality signal detection
Decades Of Data & AI Delivery
The scale and experience behind every engagement, from strategy through production.
How much authority should an agent actually have?
Decided per workflow, with risk and the business owner, before anything is built. Most enterprise agents should not start at the top of this ladder.
Watches and reports. Takes no action at all.
Zero riskProposes an action. A person decides and executes it themselves.
Advisory onlyAssembles everything and stages the action. A person approves, and the agent executes on approval.
Recommended startExecutes autonomously inside defined thresholds, escalating anything outside them.
After evaluationFull autonomy for the workflow, with monitoring, sampling and reversal available.
High volume, low varianceIn regulated environments, operating at A2 with analyst approval is not a limitation. It is the expected posture, and it is what gets a system through a risk committee.
An agent without governance is a liability.
Our agentic AI services incorporate these eight controls when an agent can access data or act in enterprise systems.
Each agent has its own identity and permission scope, inherited from your identity provider.
An agent cannot reach data a person in the equivalent role could not reach.
What it may do, to which records, up to which thresholds, declared in policy-as-code.
Defined points where authority returns to a person before anything irreversible happens.
Every consequential action has a defined path to override, unwind or correct it.
Golden sets and regression gates, so quality is measured rather than assumed.
Every step, tool call and retrieved source retained and searchable for audit.
Per-workflow budgets and routing, so an agent cannot quietly become expensive.
Agents built for workflows we actually know.
- RFQ intake, quote generation and bid / no-bid assessment
- Dealer credit decisioning, claims and warranty intelligence
- Parts, service and troubleshooting agents
- Tariff impact, customs audit and trade compliance
- Order, inventory and fulfilment exception handling
- Loan origination, boarding and condition resolution
- AML alert investigation and financial crime triage
- Reconciliation and break resolution
- Investment research and advisory preparation
- Servicing, collections and customer operations
Real Outcomes, Delivered at Scale
See how we help enterprises apply AI to improve operations, decision-making and customer experiences.
Transforming Dealer & Customer Support with AI
An AI-powered knowledge platform streamlined dealer and customer support by delivering faster, context-aware responses and reducing manual effort.
Response
AI Virtual CFO Platform for Smarter Financial Decisions
A multi-tenant AI platform consolidated financial data, automated reporting, and delivered advisor-level insights for thousands of concurrent users.
Reporting
From Customer Requirements to Engineering-Ready Designs
An AI-powered platform converted customer requirements into engineering-ready design inputs, reducing design iterations and accelerating production readiness.
Iterations
Decision Intelligence Agents for Financial Institutions
A unified AI finance engine integrated ERP and credit models to deliver real-time liquidity insights and automated working capital decisions.
Decisioning
Technology Supporting Our Agentic AI
Our agentic builds span models, orchestration frameworks, tool integrations, data platforms, governance controls, and deployment environments.
The IX AI Foundry provides reusable capabilities for planning, memory, evaluation, governance, security, and operational control.
Python
TypeScript
Go
LangChain
AutoGen
CrewAI
LangGraph
GPT-4
Claude
Llama 3
Mistral AI
Pinecone
ChromaDB
FAISS
Weaviate
CrewAI
AutoGPT
BabyAGI
AutoGen AI
AWS Bedrock
Vertex AI
Azure OpenAI
NVIDIA DGX
DeepSpeed
TensorRT
ONNX
Guardrails AI
Moderation API
Voyager
SWARM AI
CAMEL
SK-LLM
OpenCV
YOLO
TF VisionAgentic AI Development Services FAQs
Software that pursues a goal by planning its own steps, using tools to read from and write to real systems, and deciding when to act and when to hand back to a person. The distinction from generative AI is authority: a generative application produces an output a human acts on, while an agent takes the action itself.
A chatbot answers. A knowledge assistant answers from your own content with citations. A copilot drafts and a person submits. An agent plans the steps, calls the systems, completes the work and escalates only the exceptions. Each level has different governance, cost and build requirements.
Several agents, each responsible for one domain or system, coordinated by an orchestrator holding the overall goal. It is useful where a process spans systems that were never designed to work together, and it is harder to evaluate than a single agent, so it should be used where the process genuinely requires it.
Decided per workflow with risk and the business owner before anything is built. We use a five-level ladder from observe through to full autonomy, and recommend most enterprise agents start at "recommend and prepare", where the agent stages everything and a person approves. In regulated environments that posture is expected rather than limiting.
Agent identity with scoped entitlements inherited from your access model, action boundaries declared in policy-as-code, human checkpoints before anything irreversible, defined reversal paths, continuous evaluation and full traceability. Controls live in the architecture rather than in a policy document.
Yes, and that is usually the hard part rather than the model. Agents read from and write to your existing systems of record through the Enterprise Integration Fabric, under the same permission model your people work within. No rip-and-replace.
An evaluation harness built during the engagement: golden sets of real cases with correct answers, trajectory tests that check the whole sequence of steps rather than only the final output, and regression gates that must pass before any release.
Eight to sixteen weeks to a first production release for a well-scoped workflow. Read-only patterns are faster. Anything writing into a system of record or affecting a customer decision takes longer, because integration and control design dominate the effort.
Agents that are deployed and never evaluated degrade quietly. Either we operate it through AgentOps and AI Managed Services, or your team does with the runbooks, evaluation sets and documentation we hand over. Somebody has to own it either way.
We are model independent. Commercial, open-weight and private models sit behind the IX AI Gateway with routing, fallback and caching, so a reasoning-heavy planning step and a high-volume classification step can use different models at different costs without a rebuild.