A ground-up framework for organizations ready to deploy agents that actually work — coordinated, trusted, and built to last. Click any layer to go deep.
Platform layers · foundation up
01
Identity & Memory Foundation
The ground floor everything else stands on. Agents without stable identity and persistent memory are just fancy API calls.
Trust is architectural. Controls live inside every layer — not as an audit wrapper bolted on after the fact.
Responsible AI controlsCompliance mappingHuman oversight hooksValue assurance
A2A First-ClassAgent-to-agent coordination gets its own governance lane, identity model, and routing fabric — not a footnote in the interface row. Learn more →
Connected platforms
SF
Salesforce
MCP · A2A · Deep dive →
SN
ServiceNow
MCP · A2A
DB
Databricks
Data · ML
MS
Teams
Comms · A2A
JI
Jira
Workflow
LG
Legacy systems
Kafka ingest
"Every other framework starts with applications at the top and treats identity, memory, and governance as optional add-ons. This one inverts the stack — because an agent without stable identity and persistent memory isn't an agent. It's a chatbot in a trenchcoat."
The inversion that changes everything
Overview / Layer 01
Layer 01 · Foundation
Identity & Memory Foundation
The thing most enterprise agentic deployments get wrong. Without stable identity and persistent memory, everything you build above this layer is structurally unsound.
Why this layer is non-negotiable
The core problem
Every time a stateless agent handles a request, it starts from zero. No memory of prior interactions, no persistent identity, no continuity of context. It's like hiring a brilliant contractor who shows up every morning with amnesia. Technically capable — operationally useless at scale.
Agent identity model
Identity registry
Every agent gets a stable, unique identity — persisted across sessions, scoped to its role. Not a session token. A first-class citizen in the system with its own permissions, history, and trust profile.
Persistent IDRole-scoped
Authorization & permissions
What tools can this agent call? What data can it touch? Permissions flow from identity — enforced at runtime, not assumed at design time. Least privilege by default.
Runtime enforcedLeast privilege
A2A trust credentials
When Agent A calls Agent B, B needs to verify A's identity. Without a credential model, A2A is a security hole dressed up as a feature. Every cross-agent call requires mutual authentication.
Mutual authCross-agent
Audit trail
Every action, every tool call, every decision — stamped to an agent identity. Compliance, debugging, and governance all depend on this. You can't govern what you can't trace.
Immutable logCompliance ready
Four types of agent memory
Working memory
Active context for the current task. The agent's live scratchpad. Brilliant but temporary.
In-context · resets
Episodic memory
Records of past interactions, decisions made, outcomes observed. Gives agents a history they can learn from.
Persistent · grows
Semantic memory
Facts, domain knowledge, learned truths about the world. Shared across agents. The organization's collective intelligence.
Shared · knowledge base
Procedural memory
How to do things. Learned workflows, tool-use patterns, successful strategies. Gets better with use.
Behavioral · improves
Memory consolidation pipeline
Raw interaction
Tool calls · outputs
→
Extraction
Pull facts · patterns
→
Consolidation
Merge with memory store
→
Vector index
Semantic retrieval
→
Agent context
Injected next session
Never deferIdentity and memory are the only two layers where deferral has no recovery path. Build them right in month one, or rebuild everything in month twelve.
The inversion principle
Every other framework puts applications at the top and identity/memory at the bottom as an afterthought. This architecture inverts that. Identity and memory aren't features — they're the substrate. Everything above them inherits their properties. Get this wrong and no amount of clever tooling above it will save you.
Overview / Layer 02
Layer 02 · Infrastructure
Data & Infrastructure
Data as a participant, not a record. The engine room that powers real-time agent decisions, model hosting, and continuous learning at enterprise scale.
The shift in how data works
Data as participant
Traditional architectures treat data as a record — something you write, store, and query. Agentic systems need data to be a participant — something that flows, transforms, enriches decisions in real time, and feeds back into the agents that created it. That requires a fundamentally different infrastructure philosophy.
Core infrastructure components
Medallion architecture
Bronze → Silver → Gold data layers. Raw ingestion, cleaned/structured, business-ready. Agents work at the Gold layer but the pipeline runs continuously. Unstructured data governed at every stage — not just at the end.
Bronze · Silver · Gold
Vector & semantic stores
Embeddings of documents, agent memories, and domain knowledge. Agents retrieve context by meaning, not keyword. The infrastructure that makes semantic memory possible at scale.
EmbeddingsSemantic search
Streaming fabric
Kafka-based real-time event streams. Agents don't poll — they subscribe. Changes in Salesforce, ServiceNow, or any connected system surface as events the agent fabric can act on immediately.
KafkaEvent-driven
MLOps & model registry
Model versioning, deployment, monitoring, and retraining pipelines. Agents can use the right model for the right task — and as models improve, the platform improves without architectural changes.
Model versioningAuto-retrain
Legacy integration reality
Most enterprises have mainframes, ERP systems, and databases that aren't going anywhere. The streaming fabric handles this through Kafka-based ingestion — legacy systems emit events or get CDC (change data capture) wrappers. The agentic layer never knows a mainframe exists. It just sees a stream.
Overview / Layer 03
Layer 03 · Nervous System
A2A Communication & Coordination
Not a gateway — the nervous system of the platform. This is where agents stop being isolated tools and start being a coordinated fabric that routes work intelligently across your enterprise.
First-class layerA2A communication has its own governance lane, identity model, and routing fabric. It's not tucked into the "interfaces" row alongside an API gateway. It's architecturally distinct because the problems it solves are architecturally distinct.
How A2A coordination works
Orchestrator agentPlans · decomposes task
→
delegates subtask
→
Specialist agentExecutes · returns result
Salesforce agentCRM data · actions
↔
ServiceNow agentTickets · workflows
↔
Databricks agentAnalytics · ML results
Core A2A components
MCP protocol layer
Model Context Protocol gives agents a standardized way to discover and call tools — including other agents. One protocol, every platform. No custom integration code per system.
StandardizedTool discovery
Agent-to-agent bus
The message fabric connecting agents. Routes task delegations, result returns, and status updates between agents with delivery guarantees, backpressure handling, and dead-letter queues.
Async routingGuaranteed delivery
Task delegation router
When an orchestrator breaks a complex task into subtasks, the router finds the right specialist agent for each. Routing decisions are based on agent capability registrations — not hardcoded rules.
Capability-basedDynamic routing
Cross-platform connectors
Pre-built MCP connectors for Salesforce, ServiceNow, Databricks, Teams, Jira, and legacy systems. Agents see a unified tool surface — the connector handles translation to each platform's native API.
Pre-builtUnified surface
Why A2A trust matters more than you think
The moment Agent A can call Agent B, you have a distributed trust problem. If Agent A can impersonate Agent B — or if a malicious prompt can hijack an agent into calling tools it shouldn't — your entire fabric is compromised. A2A trust credentials (from Layer 01) are what make this safe. Every cross-agent call requires mutual authentication. No exceptions.
Overview / Layer 04
Layer 04 · Execution
Agentic Runtimes
Where agents actually execute. This layer is where reasoning meets action — tool discovery, orchestration, evaluation loops, and observability all converge here.
Runtime components
Tool registry & discovery
Agents don't have hardcoded tool lists. They query the registry at runtime: "what can I do right now?" The registry reflects current permissions, available connectors, and platform health — dynamically.
DynamicPermission-aware
Workflow orchestration
Multi-step agent workflows with branching, parallelism, and error recovery. The orchestrator coordinates without micromanaging — agents retain autonomy within defined bounds.
Parallel executionError recovery
Evaluations engine
Continuous evaluation of agent outputs against defined criteria — accuracy, safety, alignment with intent. Not a one-time test suite. A live feedback loop that surfaces drift before users notice it.
ContinuousDrift detection
Observability & tracing
Full distributed tracing across agent calls, tool invocations, and A2A delegations. When something goes wrong — and it will — you need a complete causal chain, not a log file.
Distributed traceCausal chain
Built in, not bolted on
The worst pattern in enterprise AI deployments is treating observability and evaluation as something you add after things break. By then, you don't know what broke or why. These capabilities need to be part of the runtime contract from day one — every agent execution emits traces, every output runs through evaluation, every tool call is logged to the audit trail from Layer 01.
Overview / Layer 05
Layer 05 · Application
Agentic Systems
The layer your users actually interact with. Four distinct system types — each suited to a different problem class. Choosing the wrong type for the job is one of the most common deployment mistakes.
Four system types
Enterprise productivity agents
Horizontal agents that bring AI interfaces and workflow automation to the broader organization. Low-code surfaces, broad utility. Think: summarization, drafting, data lookup across systems. High volume, moderate complexity.
Broad utilityHigh volume
Highly custom agents
Built on lower-level frameworks for developers who need full control. These agents solve the most complex problems — ones where pre-built solutions break. High capability ceiling, high investment.
Full controlComplex problems
Purpose-fit agents
Pre-built agents with light integration, tailored to a specific domain. Think: a legal review agent, a procurement agent, a claims processing agent. Best-in-class for their domain, limited outside it.
Domain-specificFast to deploy
Workflow automation agents
Process-focused agents that handle defined workflows with minimal human intervention. The AI is optional — these can run without LLMs for deterministic steps and invoke agents only when judgment is needed.
Process-drivenSelective AI
The deployment mistake everyone makes
Organizations reach for highly custom agents when purpose-fit agents would work fine — burning 6 months of engineering on problems that are already solved. The inverse also happens: deploying a purpose-fit agent for a genuinely novel problem and watching it fail at the edges. Match the system type to the problem class before you write a line of code.
Overview / Layer 06
Layer 06 · Trust
Governance & Trust
Trust is architectural. The single most important idea in this framework. Governance that lives outside your architecture is decoration. Governance that lives inside it is infrastructure.
The embedded governance principle
Most organizations treat AI governance as a review process — a committee that audits outputs after the fact. That's compliance theater. Real governance means controls are enforced at the point of action: inside the runtime, inside the identity model, inside the A2A protocol. You can't govern what you can't intercept.
Governance dimensions
Regulatory compliance
FedRAMP, FISMA, SOC2, GDPR — compliance controls mapped at the layer where data is created, not just at the perimeter where it exits. ATO pathways built into the architecture, not retrofitted before an audit.
FedRAMPFISMASOC2
Data privacy
PII detection, data residency enforcement, access scoping per agent identity. An agent processing a customer record can only see what its identity permits. Enforced by Layer 01, not by policy documents.
PII detectionResidency control
Responsible AI controls
Evaluation loops that check agent outputs for bias, harm, and alignment with intent before they reach users or trigger downstream actions. Not a human reviewer — an automated guardian that scales with the system.
Bias detectionOutput guards
Human oversight hooks
Defined escalation points where agent autonomy pauses and human judgment enters. Not a failure mode — a design choice. High-stakes decisions (financial, legal, medical) get a human in the loop by architecture, not by accident.
Escalation rulesAudit checkpoint
Value assurance
The final governance dimension — and the most underrated. Value assurance is continuous monitoring that agent behavior matches original intent over time. Models drift. Prompts shift. Connected systems change. Value assurance surfaces misalignment before it becomes a business problem, feeding corrective signals back to Layer 04 (the evaluation engine) in real time.
Overview / Salesforce + MCP
Deep Dive · Platform Integration
Salesforce + MCP
How an enterprise CRM becomes a first-class node in your agentic fabric — discoverable, callable, and composable with every other platform on the bus.
The three-step MCP pattern
Agent orchestratorReasons · plans · routes
→
1 · discover
→
Salesforce MCP serverReturns tool manifest
→
tool manifest
→
Salesforce CRMData · actions · flows
Tools exposed via MCP
QR
query_records
Natural language to SOQL. Agents query accounts, contacts, opportunities, cases — without knowing Salesforce's data model.
CC
create_case
Open a support case with full context from the agent's working memory. Service Cloud integration out of the box.
UO
update_opportunity
Move deals through pipeline stages, update amounts, attach meeting notes — all from agent context, no UI required.
TF
trigger_flow
Fire any Salesforce automation flow from an agent action. The full power of Flow Builder, accessible to any MCP client.
Why the orchestrator doesn't know it's Salesforce
The abstraction that matters
The orchestrating agent never imports a Salesforce SDK or knows anything about SOQL, Apex, or the Salesforce data model. It just knows how to talk MCP. Swap Salesforce for ServiceNow, Jira, or a legacy mainframe — same agent, same protocol, different tools in the manifest. This is what makes the platform genuinely composable rather than just "integrated."
Where results go after Salesforce
Salesforce result
Structured data returned
→
Agent reasoning
Interprets · decides
→
A2A bus
Routes to next agent
→
ServiceNow agent
Opens related ticket
→
Teams notification
Human informed
Salesforce isn't a destination. It's a node.
The real enterprise value isn't "agents can query Salesforce." It's that a Salesforce query result can automatically trigger a ServiceNow ticket, update a Databricks pipeline, notify a Teams channel, and update a Jira epic — all without a human routing anything. That's the A2A layer doing its job, with Salesforce as one node in an intelligent fabric.