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Core AI · Module 01 / Agents

Autonomous work,
coordinated intelligence.

Deploy teams of specialised agents — planners, researchers, executors and critics — that reason together, use your tools and complete real business work end-to-end.

Explore the moduleSee it in action
5–40
agents per workflow
94%
task completion
238ms
p95 handoff
clickripple · orchestrator · renewal-copilot
LIVEus-east
Multi-agent maptask · req_38a1
PlannerResearcherExecutorCriticPolicyOrchestratorv14 · live
Task queue
  • Plannerrunning
    Decompose renewal review
  • Researcherqueued
    Fetch policy · precedent
  • Executorqueued
    Call CRM · pricing API
  • Criticqueued
    Validate against SLA
  • Policyqueued
    SOC2 · residency check
HomeCore AIAI Agents
What it does

Agents don't replace teams. They become one — with judgement, memory and access to the systems that matter.

A single request enters the orchestrator. Roles get dispatched. Each agent runs on the right model, calls the right tool and defers to a critic before anything reaches production. What you see is a workflow. What we ship is a supervised team.

01
Coordinated intelligence
Typed message bus, shared memory and role-based agents — not brittle chains of prompts.
02
Real tool-use
Agents call your CRM, ERP, SQL, pricing APIs and internal services under signed policy.
03
Supervised autonomy
Every action is inspected by a critic and a policy guard before it commits.
How it works

One continuous flow from signal to outcome.

Every stage is instrumented, observable and safe to swap. No timelines. No boxes to connect by hand.

  1. Stage 1
    Signal
    trigger
    A ticket, event or API call arrives from your system of record.
  2. Stage 2
    Plan
    planner agent
    The task is decomposed into typed sub-goals with explicit success criteria.
  3. Stage 3
    Act
    workers + tools
    Executor agents call the right tools with retries, timeouts and traces.
  4. Stage 4
    Critique
    critic + policy
    A critic model and policy layer verify quality, safety and residency.
  5. Stage 5
    Outcome
    commit
    The final action is written back to your systems, with a signed audit log.
Live orchestration

Watch a team of agents
finish real work.

orchestrator · run · renewal-copilot · req_38a1
LIVEus-east
Agents
  • Planner#1
  • Researcher#2
  • Executor#3
  • Critic#4
  • Policy#5
  • Orchestrator#6
Tools
crm.query
pricing.api
sql.docs
policy.check
notify.slack
erp.write
Coordination log
  1. Planner→Researcher12ms
    Pull the last 12 months of contract renewals for tier-1 accounts.
  2. Researcher→Executor48ms
    24 accounts flagged. 7 have upcoming price changes.
  3. Executor→Critic132ms
    Draft renewal proposal · Account 882 · +6% uplift.
  4. Critic→Policy168ms
    Cross-check against SLA · discount matrix.
  5. Policy→Orchestrator210ms
    PASS · SOC2 · residency=us-east.
  6. Orchestrator→System238ms
    Send draft to human reviewer · notify AE.
Plan
  1. Identify renewals
  2. Score risk
  3. Draft proposal
  4. Policy review
  5. Send to human
  6. Track response
Outcome
$1.4M
forecast uplift · tier-1
Enterprise applications

Where ai agents earns its keep.

Financial services
Financial services
Case · 01 / 04

Renewal & pricing copilots for tier-1 accounts.

AI capability
Planner · pricing tools · policy guard.
Business result
+$1.4M uplift per quarter · −72% AE prep time.
Explore this scenario
Performance

Numbers that earn deployment.

94%
Task completion
measured on real production workflows
3×
Faster decisions
human-in-the-loop reviews
72%
Less manual work
reclaimed across ops teams
99.9%
Policy compliance
signed audit log per action
Compatibility

Meets your stack where it lives.

Bring your own models, cloud and data. Everything speaks the same runtime contract.

Reasoning models
cr-reason-1GPT-4.1Claude 4Llama 4 · 70BGemini 2.5
Business systems
SalesforceSAPWorkdayServiceNowNetSuiteSnowflake
Cloud & runtime
AWSAzureGCPOn-premAir-gapped
Governance
SOC 2HIPAAGDPRPII redactionSigned traces
Frequently asked

Straight answers.

Chatbots respond. Agents complete work — they plan, call tools, cross-check with critics and commit changes to your systems under signed policy.
Each role runs on the right model for the job. Reasoning uses cr-reason-1 (or your preferred frontier model), retrieval uses cr-embed-4, and safety uses cr-guard-1.
Every action is inspected by a critic model and a policy layer that enforces PII redaction, residency and jailbreak detection before commit.
Yes. Agents run inside your VPC or ours, use your existing IAM and call your APIs with signed, typed tool contracts.
A first supervised workflow ships in 3–6 weeks. Additional workflows compound on the same orchestrator.
AI Agents

Deploy this module in your enterprise.

Talk to an AI engineer. Ship a working proof-of-value in weeks, not quarters.

Book a working session