APIs designed for agent traffic and MCP tool calls.
AI agents are API consumers and API producers. Designing the API layer an agent calls and the API layer it exposes is half the work of building the agent. We design APIs for agent traffic — high concurrency, structured failures, idempotent retries.
For the last decade, API design was primarily a problem of serving browser clients and mobile apps. The consumers were humans, interacting through interfaces, at human speed. The AI era introduces a new consumer: agents that call APIs at machine speed, without a human reviewing each call, and that need deterministic, machine-readable responses to chain into automated workflows.
This changes API design requirements in specific ways. Error codes need to be machine-readable, not just human-readable. Response schemas need to be strict — agents do not handle ambiguity gracefully. Rate limits need to accommodate burst patterns from agent workflows, not just human interaction patterns. And increasingly, APIs need to be discoverable by agents via MCP tool descriptions.
Model Context Protocol solves the integration problem for AI agents. Before MCP, integrating a new capability into an agent meant writing a custom tool function, testing it against the specific model being used, and repeating that work for every agent framework. With MCP, you build one server that exposes your capabilities according to the protocol, and every MCP-compatible agent can use them.
The most important part of an MCP tool is the description field. This is the text the LLM reads to decide whether to invoke the tool and how to use it. A vague description leads to incorrect tool use. A precise description — including what the tool does, what parameters it expects, and what it returns — makes the tool reliably useful across different models and contexts.
GraphQL had its moment of maximum adoption around 2021-2022. By 2026, the consensus has settled into something more nuanced: GraphQL is genuinely better than REST for complex, relationship-heavy data models served to UIs with variable data requirements. It is not better for simple APIs, external-facing surfaces, or agent consumption.
Agents in particular struggle with GraphQL's query flexibility — they need a simpler, more constrained interface. REST with strong OpenAPI specifications gives agents (and developers) a clear, discoverable contract. For internal service-to-service communication at scale, gRPC provides better performance and stronger typing than either.
In 2026, your API surface has a new consumer class: AI agents that call at high frequency, expect deterministic responses, and don't have a human reviewing each request. Most existing API designs weren't built for this, which creates brittleness exactly where AI workflows depend on reliability. We design API architectures that serve the full consumer spectrum — browser clients, mobile apps, internal services, and agent frameworks — without compromising any of them.
That means REST with OpenAPI contracts for external surfaces, gRPC for internal service communication, and MCP servers for exposing capabilities directly to AI agents. For AI-powered endpoints, SSE streaming is the default, not an afterthought. Every third-party dependency gets a circuit breaker, a retry policy, and a fallback path — because external API failures are when, not if.
We identify every consumer of each API surface: browser clients, mobile apps, internal services, AI agents, and external partners. Each consumer class has different requirements for response structure, error tolerance, and rate behavior — and those differences drive design decisions early.
We define API contracts before writing implementation code: OpenAPI for REST, protobuf for gRPC, GraphQL schema for graph APIs. Machine-readable contracts enable code generation, client SDKs, documentation, and agent tool descriptions from a single source of truth.
We identify which capabilities should be exposed as MCP tools and write the tool name, description, input schema, and output contract for each. The tool description is what the LLM reads to decide whether to invoke it — quality here determines whether agents use your tools correctly.
We design the layer that wraps third-party API dependencies: authentication management, circuit breakers, retry policies, timeout budgets, and fallback behavior. Every external dependency is a potential failure point — the architecture limits how far that failure travels.
For AI-powered endpoints, we implement server-sent events so responses stream token-by-token as the model generates them. Buffering a 30-second LLM response before returning it is a latency problem that compounds at scale — we don't build it that way.
We instrument every endpoint with latency percentiles, error rates by consumer, and request identity tracking. Rate limits are tuned to protect the service without blocking legitimate high-frequency consumers like agent orchestration workflows.
Tools and infrastructure we use for this capability.
Agents call APIs in bursts, in parallel, and with retry-on-error. We design endpoints to be idempotent, rate-limit aware, and return errors agents can actually recover from.
For internal capabilities you want every future agent to be able to use, we expose them as MCP servers — so any model, any framework, any agent can call them without a custom integration.
OpenAPI specs, generated client SDKs, semantic versioning, and a changelog. Agents and humans alike consume APIs better when the contract is explicit.
Model Context Protocol is an open standard that defines how AI models discover and invoke tools. An MCP server exposes your capabilities — API actions, database queries, business logic — in a format any MCP-compatible agent can use without custom integration code per framework. Build it once; every AI framework with MCP support (LangChain, Claude, OpenAI Agents, CrewAI) can consume it without additional work.
REST with OpenAPI is the right default for external APIs, partner integrations, and agent-facing surfaces — the tooling and framework support strongly favor it. GraphQL earns its complexity only for UIs with highly variable data requirements across a complex domain model. gRPC is the right choice for internal service communication where you need low latency, strong typing, and high throughput — not for external-facing endpoints.
Server-Sent Events (SSE) is the standard pattern: the server sends a text/event-stream of chunks as the model generates them, and the client renders them progressively. SSE is simpler than WebSockets for unidirectional streaming and is natively supported in all modern browsers. We implement it with proper error handling, connection keepalive, and automatic client reconnection.
Yes — we build an MCP adapter layer over your existing REST API that exposes current endpoints as agent tools, with tool descriptions and input/output schemas added as a wrapper. The underlying services don't change. This is usually the fastest path to agent compatibility for organizations that have established API surfaces and don't want to touch core services.
No resilience layer on third-party dependencies. Teams build direct integrations with external services — payment processors, data APIs, auth providers — and when those services have an outage or start rate-limiting, the failure propagates straight into the product. A circuit breaker that opens after repeated failures and returns a degraded-but-functional response is a small implementation investment that prevents a significant category of production incidents.
Anonymized engagements with real outcomes — no client names per NDA.
19%
Energy Cost Reduction
99.1%
Sensor Data Uptime
4.2s
Alert Latency
“We were making energy management decisions from monthly utility bills. Having real-time sensor data and anomaly detection changed what was even possible — we caught equipment inefficiency that had been running for years without anyone knowing.”
— Head of Facilities Operations, Manufacturing Conglomerate
18%
Fuel Cost Reduction
96.5%
GPS Uptime
14%
Empty Mile Reduction
“The empty mile reduction paid for the system within the first two months of operation. The dispatch team now has real information to make decisions from instead of relying on driver phone calls.”
— Operations Director, Logistics Company
2.1x
Time on Page
38%
More Qualified Leads
52%
Fewer No-Shows
“The no-show reduction was the metric our agents cared about most. The buyers who book visits after exploring the virtual tour have already self-selected — they know the property and they are serious.”
— Head of Digital, Real Estate Platform
API Architecture & Integration sits beneath the services we sell and the agents we ship. If you are scoping outcomes rather than tools, start with one of these.
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