Specific solution · AI + Machine Learning
Agentic AI systems
Give AI agents useful work—and clear limits.
EINO builds supervised AI agents that plan tasks, use approved tools, coordinate work across systems, and stop for human decisions when the risk requires it. Every action can be traced, tested, and owned.
Evidence framework
Define proof before production.
Baselines and targets are agreed for each engagement. These dimensions shape what the team evaluates; they are not promised historical results.
Useful
Does it complete the right task?
Test the whole run with representative requests, systems, users, and exceptions.
- Task completion
- Cycle time
- Human effort
- Handoff quality
Trustworthy
Does it stay within authority?
Check tool choice, policy rules, approval paths, and whether the agent stops safely.
- Tool accuracy
- Policy adherence
- Approval routing
- Safe-stop quality
Operable
Can teams see and control it?
Track every run, cost, failure, recovery, and change once the agent is in service.
- Trace coverage
- Cost per run
- Recovery rate
- Service reliability
Solution portfolio
Four ways agents can coordinate real work.
The right pattern depends on the task: one agent may run a bounded process, while complex work may need orchestration, feedback, or several specialist agents.
Intelligent Process Automation
Run repeatable processes across systems with clear exception paths.Agents can gather context, choose an approved action, update business systems, and route uncertain or high-impact cases to people.
Technical capability
- Workflow planning + execution
- Approved tool + API use
- Rules + decision policies
- Exception handling
- Human approval steps
Business application
- Financial operations
- Supply chain operations
- Human resources
- Compliance monitoring
- Back-office service work
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
lower HR operating costs
IBM AskHR result over four yearscommon-question containment
IBM AskHR company case studyfewer support tickets
Reported change since 2016employee interactions in one year
IBM AskHR volume in 2024Advanced Task Orchestration
Coordinate multi-step work, dependencies, tools, and handoffs.An orchestration agent can break work into steps, manage priorities and dependencies, call the right systems, and recover or escalate when a step fails.
Technical capability
- Task planning + decomposition
- Dynamic prioritization
- Dependency management
- Tool + resource selection
- Failure recovery + escalation
Business application
- Project coordination
- Data pipeline operations
- Customer journey orchestration
- IT operations
- Research workflows
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
to deploy an agent
Down from months in a Google Cloud casefor audience research
Down from two days in the same casefor a contract negotiation plan
Custom-agent workflow case resultemployee onboarding time
Down from weeks in the same caseAdaptive Learning Systems
Improve decisions from observed outcomes without losing release control.Adaptive systems use feedback and changing operating signals to update recommendations or control policies. Testing and staged release keep learning separate from uncontrolled change.
Technical capability
- Outcome + feedback capture
- Performance evaluation
- Environment + drift detection
- Controlled retraining
- Simulation + staged release
Business application
- Customer behavior prediction
- Market intelligence
- Risk management
- Quality assurance
- Industrial control
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
less cooling energy
Google data-centre ML deploymentless overall PUE overhead
Reported in the same live deploymenthigher wind-energy value
Google DeepMind forecasting programless cooling electricity
Later Google collaboration summaryMulti-Agent Coordination
Assign specialist agents to work in parallel under one supervisor.A supervisor agent can divide work, give specialist agents distinct tools and context, resolve dependencies, and combine their results into one reviewable outcome.
Technical capability
- Supervisor + specialist roles
- Task delegation
- Shared state + progress tracking
- Conflict + dependency handling
- Result synthesis + review
Business application
- Research + development
- Manufacturing optimization
- Security operations
- Trading operations
- Complex enterprise analysis
Published results + market benchmarks
External research and case-study benchmarks. Results vary by use case.
better research-eval performance
Multi-agent vs. single-agent internal evaluationshorter complex research time
Parallel subagent + tool executionof performance variance explained
Tokens, tool calls, and model choice in one evaluationmore tokens than chat
Published multi-agent cost tradeoffView research sources (5)
These are external benchmarks, estimates, and published case-study results—not guaranteed EINO outcomes. Results depend on scope, system conditions, implementation, and operating context.
- 01Read source
Need optimized HR support? AskHR
IBM reports company-wide cost, containment, ticket-volume, and interaction results for its internal HR agent. Results reflect one long-running implementation.
- 02Read source
Hallam speeds up customer discoverability with a systematic AI strategy
A Google Cloud customer case reporting deployment, research, contract-planning, and employee-onboarding times for custom agents.
- 03Read source
DeepMind AI reduces Google data-centre cooling bill by 40%
Results from a live Google data-centre deployment using neural networks to recommend operating actions within defined constraints.
- 04Read source
Advanced machine learning helps Play Store users discover personalized apps
A summary of applied ML programs, including data-centre cooling and wind-energy forecasting results.
- 05Read source
How we built our multi-agent research system
Internal evaluation results and operating tradeoffs from a production multi-agent research system; the benchmark is not a general business-performance claim.
Operating contexts
The constraints shape the system.
Information sensitivity, decision authority, service expectations, and review obligations change what a responsible implementation requires.
Financial services
Agents may prepare research, reconcile records, or route exceptions, but transaction limits, approvals, and full action histories must stay explicit.
- Operations
- Research
- Risk review
Government + public sector
Casework and service operations need permission-aware tools, clear reasons for actions, accessible review, and retained human authority.
- Case management
- Citizen services
- Document workflows
Enterprise + industrial operations
Agents working across supply chains, IT, or physical operations need safe states, reliable system interfaces, and fast human escalation.
- Supply chain
- IT operations
- Industrial workflows
Enterprise architecture
An agent needs a control system around it.
Production agentic AI connects requests, plans, business tools, approvals, and action records. The model is one part of that operating path.
Request + context
A user, event, or system starts a run with identity, task scope, business context, and operating limits.
Plan + assign
The agent breaks the goal into steps, checks dependencies, and assigns work to tools or specialist agents.
Retrieve + use tools
Permission-aware knowledge, APIs, and business applications provide the evidence and actions needed for each step.
Validate + approve
Policy checks, evaluations, and human review test proposed actions before consequential work proceeds.
Act + record
Accepted actions enter the workflow with a complete trace, outcome signals, and a safe path for recovery.
Controls that cross the system
- Identity + least privilege
- Action logs + traces
- Evaluations + policy
- Budgets + rate limits
- Human authority
Deployment patterns
- Managed agent platforms
- Private cloud
- Hybrid
- On-premises
Selected around data, integration, control, performance, and ownership requirements.
Agent design
Give agents only the autonomy the task can support.
Start with a narrow task and limited tools. Add independent action or more agents only when testing shows a clear benefit and the controls can support it.
Human authority by design
Approval rules, spending limits, safe stops, and named owners keep consequential decisions with accountable people.
- 01
Constrain
Workflow + rules first
Use a known process, fixed tools, and explicit decision rules when the task does not need open-ended planning.
- 02
Assist
Agent proposes, person decides
Let the agent prepare plans or actions while an accountable person reviews every consequential step.
- 03
Act
Bounded independent action
Allow reversible, low-risk actions only after the task passes evaluations and failure paths are proven.
- 04
Coordinate
Supervisor + specialist agents
Add multiple agents when work is genuinely parallel, roles are distinct, and the performance gain justifies added cost and complexity.
Delivery path
From a bounded task to an operated agent.
Five steps connect the business objective, permitted actions, test evidence, and operational ownership before autonomy expands.
- 01FrameTask charter + boundaries
What task should the agent own?
Choose the users, trigger, desired outcome, current baseline, and actions that stay out of scope.
- 02ShapeAgent + control design
What may it see and do?
Map tools, data access, decision rights, approvals, safe stops, and named operating owners.
- 03MakeSupervised working agent
What is the smallest complete run?
Build one end-to-end task with real systems, traces, review points, and recoverable actions.
- 04ProveEvaluation + release decision
Does it act correctly under pressure?
Test representative work, tool failures, policy conflicts, ambiguous requests, cost, and escalation quality.
- 05OperateOperating model + change path
How will autonomy stay controlled?
Monitor runs, investigate failures, manage changes, review permissions, and expand scope only from evidence.
A controlled place to begin
Find the right task for an agent.
An Agentic AI opportunity assessment identifies workflows where an agent can help, the actions it may take, and the proof needed before production.
Assessment outputs
- Prioritized agentic use cases + exclusions
- Workflow, tool + data-readiness findings
- Agent architecture + autonomy options
- Evaluation, safety + approval plan
- Pilot recommendation + delivery path