Trajeckt
Runtime enforcement that blocks off-plan AI agent actions before they execute.
Tagline
Block bad agent steps before they run
Runtime policy for agent trajectories, not tool calls.
Turn agent logs into evidence you can replay.
Stop the bad step before the side effect.
The runtime policy layer for agent trajectories, not just tool calls.
The page’s strongest differentiator is sequence-aware enforcement: it repeatedly contrasts single-call guardrails with trajectory-level control, which is a clean category claim.
The alternative to postmortem logs and one-off guardrails for agent security.
Trajeckt emphasizes replay, evidence, and structured investigation; that makes it a stronger fit than generic observability or prompt-logging tools for teams that need to explain and stop agent behavior.
Stop the bad step before it becomes an irreversible side effect.
The product demo centers on blocking email.send, deploy_production, and other dangerous actions at runtime, which is a crisp pain-killer message for teams worried about accidental or malicious agent actions.
Primary user
Security or platform engineer responsible for deploying and governing AI agents in a production environment
ICP #1
Platform engineer at a mid-market SaaS company rolling out internal AI agents
Pain
They can see single tool calls, but they cannot reliably stop agents from wandering off-plan across multi-step trajectories, especially when one bad step triggers email sends, writes, or deployments.
Why this solves
Trajeckt enforces the sequence itself, so the engineer can block off-plan transitions at the gateway before the risky action executes.
ICP #2
Security architect at an enterprise using MCP-based internal agents
Pain
They need auditability and policy control for agent actions, but raw logs are too noisy to reconstruct incidents and prove what the model intended versus what actually happened.
Why this solves
Trajeckt creates a structured evidence layer with replay, decision reasons, and exportable records, which is much more useful than ordinary logs.
ICP #3
Founding engineer building customer-facing agent workflows with external side effects
Pain
They need to ship fast, but every tool call to CRM, invoices, email, or deployment systems introduces blast-radius risk and approval overhead.
Why this solves
Trajeckt lets them put approvals and plan enforcement in front of those side effects without rewriting the agent stack, since it works as a gateway for MCP and OpenAI-compatible calls.
Strengths
- +The core differentiation is crystal clear: sequence-aware runtime enforcement versus one-call guardrails.
- +The page proves the product with a concrete replay demo, blocked email step, and measurable latency numbers.
- +Integration story is strong because it shows MCP, Claude Agent SDK, LangChain, LangGraph, and OpenAI compatibility.
Weaknesses
- −The brand name and URL are inconsistent enough to create trust friction: Trajeckt on page, traject.tamor.ai in the URL, and a tamor logo in the header.
- −The copy is abstract in places and over-indexes on clever phrasing like "The sequence is" instead of immediately naming buyer pain and system impact.
- −It does not clearly state deployment model, security posture, or enterprise requirements like data retention, policy authoring, or identity integration.
- −The target buyer is implied but not explicit; a security leader, platform engineer, and AI engineer all appear, which dilutes message focus.
- −There is no concrete use-case section for the highest-value workflows, such as CRM actions, invoice processing, support workflows, or production deployments, despite those appearing in the demo.
Fix these
- Lead with one brutally specific use case, such as preventing off-plan email sending, production deploys, or invoice tampering in agent workflows.
- Replace some of the poetic copy with direct enterprise language: who buys this, what it blocks, and what systems it protects.
- Add a named policy model section explaining how plans are declared, how approvals work, and what counts as a valid transition.
- Add trust content: deployment architecture, audit/security claims, data handling, and whether the gateway is inline or sidecar.
- Tighten the brand presentation so the name, logo, domain, and product identity all match everywhere on the page.
Drop-in replacement copy
Headline
Block bad agent steps at runtime
Enforce the plan before email, deploy, or write actions fire.
Stop invalid moves before side effects happen
Trajeckt checks each tool call against the declared plan in real time. If the step is off-plan or the transition is illegal, it gets blocked before execution.
Make risky actions require human approval
Tag high-impact tools with approval policies so the agent cannot touch them alone. This gives platform and security teams a control point without rewriting the agent.
Turn every run into evidence
Get structured timelines, decision reasons, transition states, outcomes, and replay history. When something looks wrong, you can reconstruct it step by step instead of guessing from logs.
Add control to your existing stack
Trajeckt works as a gateway for MCP and OpenAI-compatible tool calls, with examples for Claude Agent SDK, LangChain, LangGraph, and the OpenAI SDK. Keep your app, add runtime policy.
FAQ
Is this just another allowlist?
No. Trajeckt checks the agent’s sequence, not just whether a tool is allowed in general. That means it can block a valid tool when it is the wrong move from the current state.
Can it work with our existing agent stack?
Yes. It is designed as a gateway for MCP and OpenAI-compatible tool calls, and the examples include Claude Agent SDK, LangChain, LangGraph, and OpenAI SDK integrations.
What happens when a risky action needs approval?
You can require human approval for specific tools or policy tiers. The agent pauses until approval is granted, so dangerous side effects do not happen automatically.
What do we get for audits and incident response?
You get a structured record of the run: decisions, transitions, outcomes, approvals, and replay history. It can also be exported as JSON for downstream systems or review.
Will this slow down agent execution?
The gateway is built to stay inline, with benchmarked latency in the low-millisecond range. The goal is enforcement without turning every tool call into a bottleneck.
Agent guardrails that only inspect single tool calls are too late. Trajeckt sits in front of the runtime and blocks off-plan steps before they execute. If the sequence is wrong, the action never fires.
Watch an agent get stopped at the exact moment it tries to leave the declared plan. No email sent. No deploy triggered. No irreversible action. That’s the point: enforce the trajectory, not just the tool.
Built Trajeckt to answer one question: can you block bad agent actions without slowing the agent down? Current p95 is under 3ms in our gateway path. Fast enough to sit inline. Strict enough to matter.
The best part of runtime enforcement is not blocking chaos. It’s proving the agent stayed on plan. Trajeckt records the full trajectory, decision reasons, transitions, and outcomes so teams can replay exactly what happened.
Trajeckt is live. It enforces declared plans at runtime, blocks off-plan tool calls before execution, and turns every run into a structured evidence layer with replay and exportable JSON. For teams shipping real side effects, this is the missing layer.
When an agent goes sideways, you do not want 400 lines of tool spam. You want the plan, the transition, the blocked step, and the reason. That’s what Trajeckt stores.
Agent: send follow-up email Declared plan: draft, review, then send Attempted step: email.send before review Trajeckt: blocked. The sequence is.
Trajeckt works as a gateway for MCP and OpenAI-compatible tool calls. That means you can add runtime enforcement without rewriting your LangChain, LangGraph, Claude Agent SDK, or OpenAI stack. Less migration. More control.
Most agent security tools look good until you put them inline. We wanted something that could actually sit in the path. So we measured it: sub-3ms p95 in the gateway path, with policy checks that still block invalid transitions.
Every agent that can email, write, deploy, or move money needs a runtime policy layer. Trajeckt enforces the sequence, supports approvals for risky tools, and gives you replayable evidence for every incident. Ship agents without pretending logs are control.
Angle: runtime enforcement for trajectory control
Most AI guardrails inspect a tool call. That is already too late. If an agent is three steps deep in a bad trajectory, the dangerous moment is not the final call - it is the invalid transition that gets it there. Trajeckt enforces the declared plan at runtime. It sits in front of agent tool calls and blocks off-plan actions before they execute. That means: - no accidental email sends - no unexpected CRM writes - no production deploys from a bad branch in the reasoning chain For security and platform teams, this matters because the question is not “did the agent call a tool?” The question is “was that move legal from where the agent stood?” That is the gap we built Trajeckt to close.
Angle: evidence layer and replay for incident response
When an agent incident happens, raw logs are usually a mess. You get tool spam, partial context, and no clean answer to the only question that matters: What did the model intend, what was allowed, and what actually ran? Trajeckt turns agent execution into a structured evidence layer. It records decisions, transitions, outcomes, approvals, and the full replay history. That gives teams something logs do not: - a step-by-step timeline - a clear blocked-step record - exportable JSON for audit or review - replay for postmortems and benchmark runs If you are responsible for agent governance, observability alone is not enough. You need control plus evidence. That is the product.
Angle: enterprise rollout without rewriting the stack
The fastest way to kill AI rollout is to ask every team to rebuild their agent stack for security. That is why Trajeckt is a gateway. It works with MCP and OpenAI-compatible tool calls, with examples across Claude Agent SDK, LangChain, LangGraph, and the OpenAI SDK. So the platform team gets runtime policy enforcement. The app team keeps shipping. The policy model is simple: - declare the plan - approve risky tools when needed - block invalid transitions before they fire - keep a record you can replay later This is the difference between “we added logging” and “we can actually govern agent behavior.” If your team is rolling out internal or customer-facing agents, the runtime layer is where trust gets built.
Tagline
Runtime enforcement for AI agent actions
Description
Trajeckt blocks off-plan agent tool calls before they execute, then records every step as replayable evidence. Built for teams shipping agents with real side effects.
Maker's first comment
We built Trajeckt after seeing the same failure mode over and over: teams would add logs, maybe a few allowlists, and then assume they had control. They did not. Once an agent is in motion, the dangerous part is often the transition itself - the moment it drifts off plan and reaches for an email send, a production deploy, or some other side effect you never intended. Trajeckt sits in front of the runtime and checks each step against the declared plan before the action fires. If the move is invalid, it gets blocked. If the move is risky, it can require approval. And if something still goes wrong, you get a structured replay with decisions, transitions, outcomes, and exportable JSON instead of a pile of raw tool logs. We built this for security engineers, platform teams, and founders who want to ship agentic workflows without pretending observability is the same thing as control.
Pinned maker comment
Would love feedback from security, platform, and AI app teams on two things: does the plan-enforcement model match how you think about agent risk, and what enterprise requirements would block adoption first?
Meta
Your agent should not email blind.
Hypothesis: platform engineers shipping internal agents need a runtime policy layer before email, CRM, and deploy actions. Trajeckt blocks off-plan tool calls before execution and records the full run for replay. If your agent can trigger side effects, this is for you.
Google Search
AI agent governance runtime
Hypothesis: security and platform teams searching for agent guardrails want something more than logs and allowlists. Trajeckt enforces declared plans at runtime, blocks invalid transitions, supports approvals, and exports replayable JSON records for incident review.
Reddit Promoted
Stop off-plan agent actions inline.
Hypothesis: developers in r/SideProject and r/indiehackers care more about preventing a bad tool call than adding another observability dashboard. Trajeckt sits in front of MCP and OpenAI-compatible tool calls, blocks invalid steps before execution, and gives you replay history when things go wrong.
Subreddits
r/SideProject
Show the blocked-step demo and ask for feedback on whether runtime enforcement is a real pain or a nice-to-have.
Rules: No pure promo; frame it as a build log, include what you learned, and invite critique.
r/indiehackers
Share the story of building a runtime layer for agent side effects and ask how others are handling agent risk.
Rules: Make it founder-journey first, product second; keep it useful and specific.
r/microsaas
Post a technical teardown of the gateway architecture and latency benchmarks.
Rules: Only share if the post includes implementation details, numbers, or a lesson.
r/EntrepreneurRideAlong
Document the first customers you’re targeting and the exact side-effect workflows you’re protecting.
Rules: Story-driven posts work best; avoid hard selling and keep it transparent.
r/LangChain
Share how runtime enforcement fits into LangChain/LangGraph agent stacks without rewriting the app.
Rules: Be technical, include integration specifics, and avoid generic marketing language.
Communities
Post build logs, incident stories, and benchmark updates. Comment on agent/security threads with specific lessons, not links.
MCP community Discord
Join the MCP implementation conversations and answer questions about gateway enforcement patterns. Share code examples only when asked.
Show how to add runtime policy enforcement to existing LangChain and LangGraph flows. Be useful in integration help channels before mentioning the product.
Cold outreach template
Hi {firstName} - saw {context} and it looks like your team is already letting agents touch real systems. Trajeckt blocks off-plan tool calls at runtime and gives you replayable evidence when something goes wrong. If useful, I can show you the blocked-step flow in 10 minutes.
Product Hunt timing
Launch on Tuesday at 12:01 AM Pacific Time. That gives you the cleanest first-day pickup from US indie founders, platform engineers, and security folks checking Product Hunt before meetings, while still leaving the rest of the workday for comments and demos.
Indie Hackers post ideas
- 01We built a runtime layer that blocks bad AI agent steps before they execute
- 02How we turned agent logs into replayable evidence instead of a wall of JSON
- 03Latency results: enforcing agent policy inline without slowing the stack
Competitor alternatives
Current tone of voice
Technical, security-forward, and slightly dramatic; for example: "The sequence is." and "The step was not a legal move from where the agent stood."
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