Everything you need to build production AI agents
From reasoning and tool use to multi-agent orchestration, observability, and enterprise security — the agentic AI platform gives builders one place to design, ship, and scale autonomous AI agents with confidence.
- 200+ integrations
- SOC 2 Type II
- Model-agnostic
One agentic AI platform, from prototype to production scale
Stitching together a model API, a tool layer, memory, and monitoring by hand is slow and brittle. These AI agent platform features replace the glue code with a runtime engineered for reliability.
Whether you are a student building your first autonomous agent or an engineering team rolling out agents across an enterprise, the platform handles the hard parts — planning, tool execution, state, and safety — so you can focus on the work the agent does.
Tasks automated
every month
Integrations
tools & APIs
Uptime
trailing 12 months
Teams building
from startups to enterprise
Reasoning, planning, and tool use that actually works
A planning engine built for real agent loops
Every agent runs the perceive → reason → act → observe loop. The platform implements proven reasoning patterns — ReAct, plan-and-execute, and reflection — so your agent decomposes a goal into steps, picks the right tool, and self-corrects when a step returns the unexpected.
Because the loop is managed for you, agents recover from tool errors and bad outputs instead of breaking, giving you robust behavior on messy, real-world tasks.
- ReAct, plan-and-execute, and reflective planning out of the box
- Automatic retries, fallbacks, and self-correction on failed steps
- Structured, schema-enforced tool calls for predictable outputs
Perceive
Read goal & context
Reason
Plan the next step
Act
Call a tool / API
Observe
Evaluate the result
200+ integrations and a typed tool layer
An agent is only as capable as the tools it can call. The platform ships with 200+ pre-built integrations — databases, SaaS apps, search, code execution, and any REST API — plus retrieval over your own data using vector stores and RAG.
Define a custom tool once with a typed schema, and every agent can call it safely. Allow-lists, scopes, and per-tool spend limits keep autonomous actions inside the boundaries you set.
- Pre-built connectors for Slack, GitHub, Postgres, Notion, and 200+ more
- RAG and vector-store retrieval over your private knowledge
- Typed custom tools with scopes, allow-lists, and rate limits
Multi-agent orchestration with delegation and review
Hard problems are easier when specialists collaborate. An orchestrator agent decomposes a goal and routes sub-tasks to worker agents — a researcher, a coder, a reviewer — then combines their results into a finished outcome.
The platform manages messaging, shared state, and handoffs between agents, so you can compose teams that delegate, parallelize, and check each other's work without writing orchestration plumbing.
- Orchestrator–worker, sequential, and parallel agent topologies
- Shared memory and structured handoffs between agents
- Reviewer agents that verify outputs before they ship
Orchestrator
Plans & delegates
Researcher
Gathers sources
Coder
Writes & runs code
Writer
Drafts output
Reviewer
Verifies results
Observability and tracing for every decision
Autonomous behavior is only safe if you can see it. Every run produces a structured trace covering each reasoning step, tool call, token count, latency, and dollar of cost — so you can debug failures, replay runs, and prove what an agent did.
Set alerts on error rates or spend, watch latency and success trends over time, and stream traces to your OpenTelemetry-compatible stack. Opaque agents become measurable systems you can operate.
- Step-by-step run traces with cost, latency, and token metrics
- Replay any run and alert on error rate or spend thresholds
- Export to OpenTelemetry and your existing dashboards
Agent success rate over time
Visual and code workflows that share one runtime
Prototype an agent in the visual builder, then drop into the TypeScript or Python SDK to add custom tools, write tests, and put it under version control. The visual graph and the code are two views of the same agent — no rewrite when you go to production.
Define an agent in a few lines, register your tools, and run it locally or in the cloud. The same definition powers the visual canvas your whole team can read.
- TypeScript and Python SDKs with a clean, typed API
- Visual builder that round-trips with code, no lock-in
- Git-friendly versioning, tests, and CI for every agent
1import { Agent, tool } from "@aiagentics/sdk";23const support = new Agent({ // define an agent4 model: "auto",5 goal: "Resolve the customer ticket",6 tools: [lookupAccount, issueRefund],7 guardrails: { humanApproval: "refunds" },8});910await support.run(ticket); // runs the agent loopEnterprise capabilities, ready on day one
The platform features that keep agents secure, governed, and dependable — without bolting on extra tools.
SOC 2 security
SOC 2 Type II compliance, encryption in transit and at rest, SSO/SAML, role-based access, and full audit logs. Enterprise data governance is built in, not added later.
Guardrails
Input and output validation, allow-listed tools, schema-enforced responses, plus spend and rate limits keep autonomous agents inside safe, predictable boundaries.
Versioning
Every agent, tool, and prompt is versioned. Diff changes, roll back instantly, and promote a tested version from staging to production with confidence.
Scheduling
Run agents on cron schedules, react to webhooks, or trigger from events. Automate recurring work without standing up your own job runner.
Human-in-the-loop
Pause an agent for approval before high-risk actions. Route decisions to a person, capture the response, and resume the run automatically.
Model-agnostic
Route each step to the best model — frontier for hard reasoning, small and fast for classification — and switch providers anytime. No vendor lock-in.
Built for the whole agent lifecycle
Design, test, deploy, observe, and govern agents in one place. The same runtime spans your first prototype and a fleet of agents handling millions of tasks — so you scale without re-platforming.
AI Agentics platform vs. build-it-yourself
You can assemble an agent stack from raw model APIs and open-source parts — but production reliability, security, and observability are where DIY gets expensive.
| Capability | Build it yourself | AI Agentics platform |
|---|---|---|
| Managed agent loop & planning | Hand-rolled | |
| Pre-built tool integrations | Few, custom-coded | 200+ connectors |
| Multi-agent orchestration | ||
| Observability & tracing | ||
| Guardrails & spend limits | ||
| SOC 2 & enterprise security | ||
| Visual + code builders | ||
| Time to first agent | Weeks | Minutes |
DIY makes sense for a weekend experiment. For agents that touch customers, money, or production systems, the platform features above — tracing, guardrails, versioning, and enterprise security — are the difference between a demo and a dependable system. See what teams build in our use-case library.
AI agent platform features, answered
The AI Agentics platform bundles everything you need to ship production AI agents: a reasoning and planning engine (ReAct, plan-and-execute, reflection), 200+ pre-built tool and API integrations, multi-agent orchestration, long-term memory backed by vector stores, full observability and tracing, visual plus code-based workflow builders, and enterprise security including SOC 2 Type II, guardrails, and human-in-the-loop approvals.
Start building production AI agents
Spin up your first agent in minutes with every feature included. Free to start — no credit card required.