Aegisora
Aegisora · Coding
Aegisora is a runtime control plane for AI agents that sits between an agent and its tools, checking every model call and tool invocation before it runs. Instead of asking the model to behave, it does policy enforcement at execution time: block risky actions, require human approval past a set threshold, and log each decision with cryptographic evidence. Teams building autonomous agents use it to keep autonomy inside defined boundaries.
About Aegisora
What Is Aegisora
Aegisora is an AI agent runtime control plane. It hooks into the path between an agent and the tools it calls, then decides what's allowed before anything actually happens. The pitch is simple: stop treating safety as something you hope the model follows, and start treating it as a gate the request has to pass. Think of agent guardrails that live in the runtime, not the prompt.
The product targets the gap that shows up once agents get real permissions. An agent wired to a database, a payment API, or a shell can do real damage in one bad call. Aegisora's answer is interception. It traces each execution, evaluates it against your policy, and returns allow, block, or pending for human review.
The main limit is scope. Aegisora governs actions; it doesn't make an agent smarter or fix a badly designed workflow. So what happens when you wire an agent to a live database and it improvises? Setup also assumes you're comfortable with code, since the current entry points are SDKs for Python and TypeScript rather than a drag-and-drop dashboard. If your team can't write policy rules, the value here is hard to reach.
Getting Started
- Sign up on the Aegisora site and get access to the SDK.
- Install the SDK for your stack (
pip install aegisora-deepagentsfor Python, or the npm package for TypeScript). - Initialize the guard with an identity for the agent and a set of policies, such as blocking PII or requiring approval above a spend limit.
- Wrap your agent's task execution so every call passes through the guard.
- Review the traces and policy decisions in the platform, then adjust rules as you learn what your agent actually does.
Product Information
A quick look at Aegisora's pricing, supported platforms, and performance.
Best for
The users, tasks, and scenarios where this tool fits best.
Users
- Platform and backend engineers
- Security and compliance leads
- Startups deploying customer-facing agents
Tasks
- Blocking PII from leaving through a tool call
- Gating high-value actions behind human review
- Tracing agent runs for debugging
- Running evaluations on prompts
Scenarios
- Shipping an agent that touches payments or production data
- Auditing agent behavior after an incident
- Testing a new prompt before rollout
Key features
Pre-execution interception
Aegisora checks each tool invocation before it runs, not after. That timing is the whole point. A false block costs you a retry; a missed destructive call costs you data. The guard returns a decision, and blocked calls never reach the tool. This tool call interception is what separates it from tools that only report on trouble after the fact.
Policy-based controls
You define rules like RequireApproval or BlockPII, and the runtime enforces them. The framing here is that policy, not the model, decides. That matters when the model has been talked into something it shouldn't do, since a prompt-level instruction can be argued around and a hard policy can't. It's least privilege for agents, applied at the moment of action.
Cryptographic audit logs
Every decision lands in a log designed to be tamper-evident. For teams that get asked "what did the agent do at 3 a.m.," this is the part that answers the question. It turns a vague claim about safety into a record you can hand to someone else.
Full execution traces
Agents rarely fail the same way twice. Aegisora shows the prompt, the model, and the tool behind each outcome, so you can trace a bad result back to its source instead of guessing which step went sideways. Call it agent observability with teeth.
Evaluation runs
You can run automated evaluations across thousands of traces and compare against a baseline. The practical use is comparing two prompts before you ship the change, rather than finding out in production that the new one is worse.
Multi-part stack
Aegisora splits into Core (build agents), Engine (observability and evaluation), and Shield (deployment gateway with sandboxes and credential handling). You can adopt one piece at a time. They still work best together.
Framework and language coverage
The SDKs cover Python and TypeScript, with hooks for common agent frameworks. That means you can add the guard to an existing setup instead of rebuilding around a new framework.
Pros and cons
Pros
- Enforces limits at execution time, so a model can't talk its way past a rule.
- Keeps signed audit logs that hold up when you need to explain agent behavior.
- Lets you pause high-value actions for human approval instead of blocking them outright.
- Splits into build, observe, and secure layers, so you can start with one.
- SDK-based integration means you don't rebuild your agent from scratch.
Cons
- No public pricing, so you have to talk to the team before you can budget for it.
- Setup assumes coding skill, and there's no click-to-configure path for non-developers.
- It governs actions but won't fix a poorly designed agent workflow underneath.
- Much of the detail lives in docs and SDK references, so expect to read before you ship.
Frequently asked questions
It intercepts an AI agent's tool and model calls before they execute, checks each one against your policy, and either allows it, blocks it, or holds it for human approval. Every decision gets logged.
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