Agent Platforms on MCP: Orchestration, Memory, and Tooling at Scale

1) What Is an MCP-Based Agent Platform?

An agent platform is more than a single agent. It’s a standardized way for multiple agents and apps to discover tools, plan workflows, share memory, and execute actions safely. With MCP, agents connect to MCP servers that expose tools (actions) and resources (read-only context) via JSON schemas. The result is a universal, governed interface between models and enterprise capabilities.

• Uniform execution: Agents call tools with validated inputs—no brittle prompt-built HTTP.
• Discoverability: Handshake exposes a live catalog of tools/resources/prompts.
• Governance: Gateways/mesh add auth, quotas, schema validation, and audit trails.
• Evolvability: Side-by-side versions (_v1, _v2) and deprecation signals.

2) Orchestration Models That Work

Agents can plan and call tools in different ways. Pick the model that fits your risk and latency needs.

2.1 Plan–Act–Reflect (Single-Agent)

// High-level cycle
1) PLAN: Generate a tool call plan with rationale
2) ACT: Execute tools with schema-validated inputs
3) REFLECT: Evaluate results, fix errors, continue or stop

2.2 DAG (Task Graph)

Represent a workflow as a Directed Acyclic Graph; nodes are tool calls, edges are data dependencies.

{
```

"nodes": \[
{"id":"reserve","tool":"reserveInventory","out":"rsv"},
{"id":"charge","tool":"processPayment","in":{"order":"rsv.orderId"}},
{"id":"notify","tool":"sendNotification","in":{"userId":"rsv.userId"}}
],
"edges": \[\["reserve","charge"],\["reserve","notify"]]
}

2.3 Event-Driven Orchestration

Emit events from tool results; subscribe other tools/agents to react (e.g., “payment.captured” triggers fulfillment).

2.4 Human-in-the-Loop Gates

Require approval for high-risk tools (refunds, deletes). Agents attach a preview; reviewers approve/deny.

{
```

"tool":"refundOrder",
"previewOnly": true,
"input": { "orderId":"A123","amount":{"amount":119.00,"currency":"USD"} }
}

3) Memory & Context: Short-Term, Long-Term, and Shared

Good memory design keeps agents accurate, fast, and low-cost.

• Short-term (session): Conversation state, partial plans, last tool outputs—ephemeral, small.
• Long-term (semantic/episodic): Summaries, key facts, decisions; retrieved on demand.
• Shared memory (team-level): Project state, runbooks, policies—read-mostly, curated.

Resource Definition for Memory (Conceptual)

{
  "name":"sessionMemory",
  "params":{"sessionId":"string","query":"string"},
  "schema":{
    "type":"object",
    "properties":{
      "highlights":{"type":"array","items":{"type":"string"}},
      "relatedFacts":{"type":"array","items":{"type":"string"}},
      "asOf":{"type":"string","format":"date-time"}
    },
    "required":["asOf"]
  }
}

Context Budgeting

• Summarize aggressively; store raw data in resources; pass only needed snippets to the model.
• Prefer “fetch on demand” over long prompts. Cache recent resource reads with asOf timestamps.
• Separate private (user-specific) from shared memory; apply access controls and redaction.

4) Tooling Strategy: Taxonomy, Preconditions, and Safety

Curate tools to be predictable, safe, and composable.

• Taxonomy: Classify tools as read (resource-like), write (side-effect), admin (restricted).
• Preconditions: Encode checks (e.g., “payment can run only if inventory reserved”).
• Idempotency: Require idempotencyKey for writes; safe retries.
• Risk scoring: Label tools with a risk tier to trigger approvals or extra logging.

Tool Declaration Sketch

{
  "name": "createShipment",
  "inputSchema": {...},
  "outputSchema": {...},
  "riskTier": "medium",
  "preconditions": ["reserveInventory.completed == true"],
  "idempotent": true
}

5) Multi-Agent Patterns

Scale complex work by specializing agents and coordinating with structure.

• Roles: Planner, Executor, Reviewer (auditor), and Router (dispatch by domain).
• Blackboard: Agents read/write to a shared state (resource) and coordinate via events.
• Delegation: Planner assigns subgoals; Executor calls tools; Reviewer validates results.

// Blackboard resource
{
  "name":"projectBoard",
  "params":{"projectId":"string"},
  "schema":{"type":"object","properties":{"tasks":{"type":"array"},"status":{"type":"string"}}}
}

6) Safety, Authorization & Approvals

• AuthN: OAuth2/OIDC for human flows; mTLS/workload identity for services.
• AuthZ: RBAC for simplicity; ABAC for tenant/risk context; scope access by tool.
• Approvals: Human-in-the-loop for risk tiers; store rationale with correlation IDs.
• PII/Secrets: Redact by default; never echo secrets; log safely.

OPA/Rego (Approval Gate)

package mcp.policy
default allow = false
allow {
  input.tool.riskTier == "low"
}
allow {
  input.tool.riskTier == "medium"
  input.approval == true
}
allow {
  input.tool.riskTier == "high"
  input.approval == true
  input.user.role == "manager"
}

7) Platform Tooling: Golden Paths & Templates

Make the compliant path the fastest path.

• Templates: Repo scaffolds with sample tools/resources, tests, and CI.
• CLI: “create-mcp-server” spins up a server with catalog, policies, and gateway manifests.
• Catalog sync: Generate MCP tools from OpenAPI; publish to the portal automatically.
• Policy packs: Reusable OPA and gateway policies for common risks.

$ npx create-mcp-server orders --template rest-node
✔ Catalog initialized
✔ OpenAPI mapped to tools/resources
✔ CI + linting + contract tests added
✔ Gateway policy stubs created

8) Observability, Evaluation & QA

• Logs: tool/resource, version, caller, tenant, latency, status, correlationId.
• Metrics: P50/P95 per tool, error/validation rates, retry counts, approval wait time.
• Traces: Span across plan → tool → downstream services; tag with session and user.
• Agent evals: Golden tasks, pass@K, factuality checks, policy adherence scores.

INFO mcp.session plan=dag nodes=3 corr=6f1a...
INFO mcp.call tool=createOrder_v2 latencyMs=183 status=200 user=svc-web corr=6f1a...

9) Performance & Cost: Make It Fast and Affordable

• Token discipline: Summaries > raw dumps; fetch on demand; prune plan chatter.
• Caching: Cache resource reads with asOf; batch read/write tools where possible.
• Streaming & timeouts: Stream long-running outputs; set sensible default timeouts.
• Fallbacks: If tool fails, retry safely or switch to a cheaper/backup pathway.

10) Reference Architectures

CDN/WAF → API Gateway → MCP Servers (per domain) → Provider APIs/DB/SaaS
   │            │
   │            ├─ Policy (auth, quotas, schema, approvals)
   │            └─ Observability (logs, traces, metrics, audits)
Agents/Apps → MCP Client SDK → Orchestrator (plan/DAG) → Memory store(s)

Tip: Keep MCP servers stateless; externalize session and long-term memory; autoscale workers.

11) 30–60–90 Day Rollout

Days 0–30:
• Pick a pilot workflow; define tools/resources and approval gates
• Stand up MCP server behind gateway; add logging/metrics/traces
• Build a single-agent orchestrator (Plan–Act–Reflect) with session memory

Days 31–60:
• Add DAG orchestration; introduce shared memory resource
• Onboard 3–5 tools from OpenAPI; enforce schema validation + RBAC/ABAC
• Start agent evals; add dashboards and SLOs

Days 61–90:
• Introduce multi-agent roles (Planner/Executor/Reviewer)
• Enforce approvals for medium/high risk; canary + rollback in CI/CD
• Expand to second domain; publish templates/CLI; monthly scorecards

12) FAQ

Q: Do I need separate MCP servers per domain?
A: It helps scaling and isolation. Use one per major domain (orders, support), then register them with the platform.

Q: Can I keep using my API gateway?
A: Yes—treat MCP endpoints like APIs: JWT, rate limits, schema checks, WAF.

Q: How do I keep costs in check?
A: Cache reads, batch calls, summarize context, and prune verbose planning.

13) Final Checklist

• ✅ Clear orchestration model (Plan–Act–Reflect, DAG, events)
• ✅ Memory layers (session, long-term, shared) with access controls
• ✅ Tool taxonomy, preconditions, idempotency, and risk tiers
• ✅ Multi-agent roles + blackboard or events for coordination
• ✅ AuthN/Z, approvals, redaction, and complete auditing
• ✅ Templates/CLI, OpenAPI→MCP generation, CI/CD checks
• ✅ Observability + agent evals; SLOs, canary, rollback
• ✅ Cost controls: caching, batching, summarization, timeouts