As large language models (LLMs) evolve from static responders into autonomous actors, developers are facing a new kind of systems challenge: building infrastructure that can support reasoning, decision-making, and continuous action. Gravity’s agentic AI platform is one of the most advanced real-world examples, a system where LLMs interact with tools, memory, and guardrails to execute complex, multi-step workflows.

Despite the challenges, developers can build a system like Gravity’s from the ground up, covering modular orchestration, behavioral safety, observability, and the integration of LLMs with business logic. Whether you’re designing intelligent assistants, AI copilots, or autonomous decision agents, these patterns will help you build something robust, transparent, and safe.

Modular orchestration with event-driven workflows

Traditional pipelines fall short when building agents that must respond to dynamic, evolving contexts. Gravity tackles this by embracing event-driven architecture and modular orchestration. Agents are modeled as independent services that react to discrete events, allowing the system to flexibly coordinate multiple actors across different stages of a task.

Technologies like Temporal, pub/sub messaging, or custom orchestrators can be used to handle event sequencing and retries. The key is decoupling logic. Instead of a monolithic agent, build a composable graph of task-specific mini-agents that can be audited independently.

To future-proof your system, define interfaces around each task. This allows you to swap out or upgrade capabilities (e.g., replacing an LLM tool-caller with a newer model) without breaking the entire flow.

Behavioral guardrails and fail-safes

Autonomous agents introduce a new class of risk: they can act in unexpected or unsafe ways. Gravity’s platform embeds multiple layers of protection. These include:

• Hard constraints that restrict the types of actions an agent can execute
• Approval checkpoints that require human validation for high-impact steps
• Fallback strategies like predefined safe states or backup heuristics

Behavioral policies are enforced both at the agent level (e.g., no repeated API calls within x minutes) and at the orchestration level (e.g., limiting how many agents are involved in a particular task).

Crucially, these aren’t just reactive guardrails. They’re designed with testability and transparency in mind, so developers can simulate edge cases, audit behavior, and refine policies over time.

Memory and context management

Unlike chatbots, agentic systems must maintain continuity across sessions. That requires memory, not just of conversation, but of tasks, tools, and prior outcomes.

Gravity uses a hybrid memory strategy:

• Short-term memory: ephemeral context (e.g., conversation state, recent tool outputs) passed between agents
• Long-term memory: persistent logs or vectorized embeddings of events, useful for recall and reflection
• Working memory: transient data structures used by agents during planning or tool execution

You can implement memory in Gravity with a combination of Redis, Pinecone, PostgreSQL, or other systems, depending on your latency and durability needs.

Memory unlocks advanced capabilities like:

• Agents reflecting on past failures
• Long-horizon planning
• Cross-session personalization

Observability and human override

When your AI makes decisions, you need to know how and why. Observability isn’t just a nice-to-have. It’s critical for debugging, compliance, and trust.

Gravity instruments its agent stack with structured logs and distributed tracing. This lets developers track:

• What prompt or input triggered an action
• What the agent “thought” or reasoned at each step
• What tools it used and what results were returned

Additionally, developers and admins have the ability to audit any agent process. This human-in-the-loop functionality acts as both a fail-safe and a learning loop, allowing teams to continuously fine-tune agent behavior.

Integrating LLMs with domain logic

LLMs are powerful, but they shouldn’t replace your business logic. Instead, they should work alongside deterministic systems that enforce rules, policies, and outcomes.

Gravity positions the LLM as a narrative engine, not a sole decision-maker. It uses LLMs to:

• Interpret goals from human inputs
• Generate potential action plans
• Query external tools and APIs

But before executing real-world actions, LLM output should be filtered through a domain-specific policy layer, often written in traditional code, that ensures compliance and accuracy.

This separation of concerns is essential. It reduces hallucination risk, makes behavior more testable, and gives you peace of mind that small prompt changes will not result in large behavior changes.

Infrastructure with intelligence

Building agentic AI systems isn’t just about chaining prompts—it’s about creating intelligent infrastructure that can reason, act, and self-correct. By borrowing proven patterns from platforms like Gravity, developers can create agents that are not only powerful but also safe, interpretable, and maintainable.

As the next generation of AI products moves beyond chat into real autonomy, systems thinking will be as important as model tuning. If you get the infrastructure right, the intelligence will follow.

Lucas Thelosen is CEO and Drew Gillson is CTO of Gravity.

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